- Access to this full-text is provided by Springer Nature.
- Learn more
Download available
Content available from Child & Youth Care Forum
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
Child & Youth Care Forum (2020) 49:661–686
https://doi.org/10.1007/s10566-020-09547-4
1 3
ORIGINAL PAPER
Foster Parent Stress asKey Factor Relating toFoster
Children’s Mental Health: A1‑Year Prospective Longitudinal
Study
AnoukGoemans1· RenateS.M.Buisman1· MitchvanGeel1· PaulVedder1
Published online: 24 April 2020
© The Author(s) 2020
Abstract
Background Foster children are reported to often have mental health difficulties. To opti-
mize foster children’s development chances, we need to know more about the characteris-
tics that are predictive of foster children’s mental health.
Objective In the current study, we aimed to establish what accounts for the differences in
foster children’s mental health, by examining the change and predictors of change in foster
children’s mental health. Insight into foster children’s mental health outcomes and their
predictors could inform the design of targeted interventions and support for foster children
and foster families.
Method In a sample of 432 foster children between 4 and 17years old (M = 10.90) we
examined a multivariate model in which characteristics of the foster child, the child’s care
experiences, foster family, and foster placement were included as predictors of foster chil-
dren’s mental health (internalizing, externalizing, and prosocial behaviors) using a three-
wave longitudinal design
Results Results showed that levels of mental health were generally stable over time. Dif-
ferences between foster children’s developmental outcomes were mainly predicted by fos-
ter parent stress.
Conclusions Foster parent stress levels were high and consistently found to be the strong-
est predictor of foster children’s mental health outcomes. Given this finding it is important
for researchers and practitioners to consider foster parent stress in screening as a point of
attention in creating conditions conducive to foster children’s mental health.
Keywords Foster care· Mental health· Foster parent stress· Longitudinal· Multilevel
* Anouk Goemans
a.goemans@fsw.leidenuniv.nl
1 Institute ofEducation andChild Studies, Leiden University, Wassenaarseweg 52, 2333AKLeiden,
TheNetherlands
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
662
Child & Youth Care Forum (2020) 49:661–686
1 3
Introduction
Foster care is a form of child welfare wherein children who cannot be raised by their own
parents are placed out-of-home and raised by foster parents. Foster care, as compared to
alternatives, most closely resembles the natural home environment of a child, providing
stability and continuity of caregivers and the opportunity to build close relationships with
substitute parent figures (Roy etal. 2000; Tizard and Hodges 1978). Although foster care is
often considered the best alternative in case of out-of-home placement (Dozier etal. 2014;
Li etal. 2017), much remains unclear about the effects of foster care on children’s devel-
opment and discussions about its efficacy are ongoing (e.g., Ainsworth and Hansen 2014;
McSherry 2018; McSherry and Malet 2017). A recent meta-analysis showed that on aver-
age foster children more often experience mental health problems than children from the
general population (Goemans etal. 2016a). However, there is large heterogeneity between
foster children with regard to their mental health outcomes (Goemans etal. 2015, 2016a).
To optimize foster children’s development, we need to know more about the characteristics
that predict to foster children’s positive developmental outcomes. Our focus is on foster
children’s mental health, because it is an important indicator of the quality of foster chil-
dren’s developmental trajectories and after care outcomes (Dixon 2008; Konijn etal. 2019;
Oosterman etal. 2007).
To examine what accounts for different developmental outcomes of foster children, it is
important to study predictors related to the foster child (e.g., age, gender), the child’s care
experiences (e.g., placement history, duration), the foster parents (e.g., parenting stress,
thinking of quitting, parenting, SES), and the foster placement (e.g., kinship or non-kin-
ship placements, planning for reunification) (Maaskant etal. 2014; Newton et al. 2000;
Vanderfaeillie etal. 2013). These predictors are considered of interest in relation to foster
children’s mental health based on what is known from both developmental theories that
are specific for vulnerable children, such as the ecological-transactional model of child
maltreatment (Cicchetti etal. 2000), as well as from broader child developmental theories
such as attachment theory (Bowlby 1969) and social learning theory (Bandura and Walters
1977). These theories and previous studies suggest that foster children’s care experiences
relate to mental health because a history of previous placements and duration of the current
placement impact children’s attachment representations (Newton etal. 2007; Rubin etal.
2007). For example, the incidence of one or more previous placements indicates the poten-
tial risk of broken attachments and unsafe attachment representations (Newton etal. 2007;
Rubin etal. 2007). Moreover, the longer the duration of the current placement, the more
likely it is that the foster child and foster parent build a safe and strong attachment rela-
tion which buffers against mental health difficulties. The current study is also informed by
Cicchetti etal.’s (2000) ecological-transactional model. According to this model, multiple
levels of a child’s ecology influence each other and in turn also influence a child’s develop-
ment (Belsky 1993; Bronfenbrenner 1979; Cicchetti and Lynch 1993; Sameroff 2009). In
order to understand foster children’s development, we should therefore not only focus on
child characteristics, but also on characteristics related to the foster family and the foster
placement. We included several foster family (e.g., foster parent’s stress, parenting, SES),
and foster placement (e.g., kinship or non-kinship) characteristics that have been shown to
be important for foster children’s mental health (Gabler etal. 2018; Winokur etal. 2018).
We also included two relatively understudied foster family and foster placement character-
istics, namely ‘planning for reunification’ and ‘thinking of quitting’. Both characteristics
are likely to impact foster parents’ and foster children’s feelings of permanency and as a
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
663
Child & Youth Care Forum (2020) 49:661–686
1 3
consequence foster children’s mental health outcomes (Rubin etal. 2007; Stott and Gus-
tavsson 2010). Foster parents who consider quitting with foster care might be less moti-
vated to continue fostering. Decreased motivation has shown to impact the foster place-
ment and consequently foster children’s mental health outcomes (e.g., Gabler etal. 2018;
Stone and Stone 1983). In addition, planning for reunification is related to foster parents’
and foster children’s feelings of permanency. If plans for reunification are made, both foster
parents and foster children realize that the foster placement is meant to be short-term. This
might impact the (investments made for the) attachment relationship (Stott and Gustavsson
2010) and consequently foster children’s mental health. The current study aimed to exam-
ine characteristics related to the foster child, the child’s care experience, the foster family,
and the foster placement in relation to foster children’s mental health. By using a longitu-
dinal design, we try to gain more insight in foster children’s mental health outcomes and
individual differences over time.
The majority of studies on foster children’s development and its predictors are of cross-
sectional nature (e.g., Clausen etal. 1998; Lehmann etal. 2013). Cross-sectional studies
can establish foster children’s functioning and examine which characteristics or circum-
stances are correlated with either desired on undesired outcomes. However, cross-sectional
studies cannot establish change and predictors for change, and hence, are unable to capture
the risk and protective factors that are linked to improvement or deterioration of foster chil-
dren’s developmental outcomes. Longitudinal research is needed to more fully understand
the developmental outcomes of foster children and to gain insight in the characteristics
or factors that predict their development (Cuddeback 2004; Holtan etal. 2005; McSherry
and Malet 2017). Several longitudinal studies on foster children’s development have been
conducted to date. The results of these studies with respect to the developmental outcomes
of foster children have not been conclusive (see for a meta-analysis Goemans etal. 2015).
Some studies found improved mental health outcomes for foster children over time (e.g.,
Ahmad etal. 2005; Barber and Delfabbro 2005; Fernandez 2009), while others did not rep-
licate these results (e.g., Leathers, Spielfogel etal. 2011; Perkins 2008) or even found that
foster children’s mental health deteriorated over time (e.g., Fanshel and Shinn 1978; Frank
1980; Lawrence etal. 2006).
Few existing longitudinal studies have focused on a combination of predictors in rela-
tion to foster children’s development (see for a good example Hiller and Clair 2018).
Simultaneously including a broad range of predictors in a multivariate model could help
to identify the strongest predictors of the development of children in foster care (Ooster-
man et al. 2007; Tarren-Sweeney and Goemans 2019). However, multivariate modeling
presents a challenge in that it requires a considerable sample size to ensure adequate power
(Tabachnick etal. 2007). Moreover, longitudinal research on children in foster care can be
difficult in terms of recruitment, data collection, and follow-up (Jackson etal. 2012; Maas-
kant 2016), and is often characterized by high attrition rates and missing data (Goemans
etal. 2015; Jackson et al. 2012; Tarren-Sweeney 2017). Advanced techniques to handle
missing data provide a solution, because especially for studies with large amounts of miss-
ing data, these techniques produce less biased estimates of missing values compared to
other more conventional methods (Graham 2009). These techniques enable both the focus
on general developmental trends as related to a single predictor, and a on a broader range
of predictors in a multivariate model (Van Oijen 2010).
In the current study, we aim to establish why some foster children have mental health
difficulties while others do not, by examining the change and predictors of change in foster
children’s mental health. We examined a multivariate model in which characteristics of the
foster child, the child’s care experiences, foster family, and foster placement are included
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
664
Child & Youth Care Forum (2020) 49:661–686
1 3
as predictors of foster children’s mental health (internalizing, externalizing, and prosocial
behaviors) using a three-wave longitudinal design and applying multiple imputation. The
inclusion of predictors in the current study is informed by developmental theories and find-
ings from previous research. We included a few relatively understudied predictors (e.g.,
planning for reunification and foster parents’ thinking of quitting) for which it is hypoth-
esized that they are predictive offoster children’s feelings of permanency and consequently
also their mental health outcomes (Rubin etal. 2007; Stott and Gustavsson 2010). Selected
characteristics related to the child’s experiences with care are placement history and dura-
tion of the placement. Selected foster family and foster placements characteristics are type
of foster family, foster parents’ thinking about quitting foster care, SES, foster parent stress,
parenting practices and strategies, and planning for reunification (Chamberlain etal. 2008;
Maaskant etal. 2014; Winokur etal. 2018). It is hypothesized that both foster child, the
child’s care experiences, foster family, and foster placement characteristics will be predic-
tive of foster children’s mental health outcomes, with the latter two being more strongly
related to the outcomes because this has been shown in previous research (Goemans etal.
2016b).
Method
Participants
Participants in this study were foster parents who completed a questionnaire on foster
child, foster family, and foster placement characteristics. They provided information on 432
foster children who resided in regular, formal foster care in the Netherlands. Foster chil-
dren (46.8% girls) were between 4 and 17years old (M = 10.90, SD = 3.81). Approximately
two thirds of the foster children resided in non-kinship foster care (66.9%). Foster chil-
dren experienced on average 1.20 previous foster placements (SD = 1.55, range 0–13), with
36.7% of the foster children experiencing no previous placements, 34.3% experiencing one
previous placement, 15.6% experiencing two previous placements, 8.0% experiencing three
previous placements, and 5.4% experiencing four or more placements. Foster children’s
mean time in the current foster placement at the first wave was 58.98months (SD = 50.61,
range 0–214months), with 19.1% of the children being in their current placement for less
than 1year, 12.3% for 1 to 2years, 11.1% for 2 to 3years, 8.9% for 3 to 4years, 8.7% for 4
to 5years, 6.5% for 5 to 6years, 6.3% for 6 to 7years, 5.1% for 7 to 8years, 4.6% for 8 to
9years, 3.1% for 9 to 10years, 3.9% for 10 to 11years, and 10.4% for more than 11years
with a maximum of almost 18years (214months).
Procedure
All foster care agencies in the Netherlands (N = 28) were invited to participate in this study.
Seven agencies (25%) agreed to participate. The participating agencies were well spread
across the Netherlands and differed on various characteristics (large vs. small, secular vs.
non-secular). The main reason for foster care agencies to not participate was to prevent
a research overload for their foster families because of their participation in other stud-
ies. Foster parents within the participating foster care agencies were informed about the
study objectives by their agency. Foster care agencies asked foster parents’ consent to par-
ticipate and we received the contact information for those foster parents who gave consent.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
665
Child & Youth Care Forum (2020) 49:661–686
1 3
In October 2014 we started our longitudinal study in which we followed foster children
and their foster families for 12months. There were three measurements, separated by six
month intervals (Wave 1: October 2014, Wave 2: April 2015, Wave 3: October 2015). To
ensure that the same foster parent completed each wave and to connect responses over the
waves to the correct participant we sent out invitations to complete the questionnaire using
a personal link. After data collection, each foster parent received a unique numerical ID
and personal data was deleted from the data file. At each wave foster parents were asked
to report the birth date of the foster child. We compared these birthdays across every col-
lected wave to ensure that foster parents had consistently reported about the same foster
child.
A total of 1387 foster families were invited to participate in the first wave of the study.
Most invitations were sent by email. However, we sent some paper questionnaires (5.2%)
to foster families for whom the email address was not known by the foster care institution.
Two reminders were sent to complete the questionnaire. All foster parents who participated
in Wave 1 were also invited to participate in both Wave 2 and Wave 3. The initial sample
that participated in Wave 1 consisted of 549 children. We excluded foster children who
resided in part-time foster care and who fell outside the age range of 3–17years, resulting
in a final sample of 432 foster children. For the goal of this paper, we only selected chil-
dren from age 4 onwards because the measure we used to measure children’s mental health
(i.e., the SDQ) is meant for children between 4 and 17years old. The participation rate was
51.6% for Wave 2 and 42.3% for Wave 3. All foster children came from different foster
families, i.e., we did not include multiple foster children who resided in the same foster
family. The [name withheld for peer review] Ethics Review Board approved the study prior
to the data collection.
Instruments
Mental Health
To measure foster children’s mental health, the Dutch version of the Strengths and Difficul-
ties Questionnaire was used (SDQ; Goodman 1997; Van Widenfelt etal. 2003). The SDQ
is a 25-item questionnaire answered on a 3-point Likert scale ranging from 0 (not true) to 2
(very true). SDQ scores were generated in SPSS using the SDQ syntax as provided on the
SDQ website (https ://www.sdqin fo.com/c1.html). The 25 items were combined into three
subscales as suggested by Goodman etal. (2010): internalizing behavior problems, exter-
nalizing behavior problems, and prosocial behavior. Higher scores on these subscales rep-
resent more internalizing and externalizing problems, and better prosocial behaviors. The
subscale internalizing behavior problems is formed by combining the ten items for emo-
tional and peer problems. Sample items are: ‘often unhappy, downhearted, or tearful’ and
‘rather solitary, tends to play alone’. The subscale externalizing behavior problems con-
sists of ten items for conduct and hyperactivity problems. Items are for example ‘generally
obedient, usually does what adults request’ and ‘easily distracted, concentration wanders’.
The subscale prosocial behavior contains five items and a sample item is: ‘Shares read-
ily with other children’. Previous research has shown good psychometric properties, both
internationally (Achenbach etal. 2008; Goodman etal. 2010; Van Widenfelt etal. 2003)
and in the Netherlands (Muris etal. 2003; Van Widenfelt etal. 2003). In the current study,
Cronbach’s alphas for the waves 1–3 were 0.79, 0.79, and 0.76 for internalizing, 0.85,
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
666
Child & Youth Care Forum (2020) 49:661–686
1 3
0.83, and 0.85 for externalizing problems, and 0.75, 0.72, and 0.77 for prosocial behavior,
respectively.
Characteristics oftheFoster Child, theChild’s Care Experiences, andtheFoster Family
Foster parents were asked to provide information about several foster child characteristics
(e.g., age, gender), the child’s care experiences (placement history, duration of the current
placement), and foster family and foster placement characteristics (e.g., SES, type of foster
family, whether foster parents were thinking about quitting foster care, planning for reuni-
fication). Whether the respondent thought about quitting was inquired with the question
“do you ever think about quitting as a foster parent?” which could be answered on a four
point Likert scale (“often”, “sometimes”, “barely”, “never”). Information about planning
for reunification was collected with the question “are there plans to reunify the child with
the biological parents?”, to be answered with “yes” or “no”. For both questions, parents
could also indicate that they did not know, which was then considered missing data. Foster
parents completed the four item Family Affluence Scale (FAS) to measure SES (Currie
et al. 1997), for which we computed a composite score ranging from 0 to 9 (M = 6.19,
SD = 1.50) (Boyce et al. 2006). The FAS has been found to be a valid measure of chil-
dren’s SES (Andersen etal. 2008; Boyce, etal. 2006). In addition, foster parents reported
their highest level of completed education. Approximately 20% of foster parents completed
primary school or secondary school. Approximately 40% completed secondary voca-
tional education, approximately 30% completed higher vocational education (university of
applied sciences), and approximately 10% holds a university degree. The information of
the FAS and foster parents’ education were standardized to ensure that both measures had
an equal weight in the composite score and subsequently combined to create one SES vari-
able, to be used as a control variable.
Parenting
The Dutch version (Van Lier and Crijnen 1999) of the Alabama Parenting Questionnaire
(APQ; Frick 1991; Shelton et al. 1996) was used to measure foster parents’ parenting
behavior. The APQ consists of 42 items that foster parents have to evaluate on a 5-point
Likert scale ranging from 1 (never) to 5 (always). The APQ measures five dimensions of
parenting: positive involvement with children (10 items, sample item: ‘You play games or
do other fun things with your child’), use of positive discipline techniques (6 items, sam-
ple item: ‘You reward or give something extra to your child for obeying you or behav-
ing well’), poor monitoring and supervision (10 items, sample item: ‘You don’t tell your
child where you are going’), inconsistency in the use of discipline (6 items, sample item:
‘The punishment you give your child depends on your mood’) and use of corporal punish-
ment (3 items, ‘You slap your child when he/she has done something wrong’). Addition-
ally, seven items deal with ‘other discipline practices’, which do not form a scale but give
additional information on parenting on an item by item basis. For the current study we
combined the first two scales (i.e., positive involvement and positive discipline) and the
other three scales (poor monitoring and supervision, inconsistency, corporal punishment)
into two new scales which reflect positive and negative parenting (Goemans etal. 2018b).
With respect to the psychometric properties, previous research has shown the APQ to be a
valid questionnaire to identify different styles of parenting (Dadds etal. 2003; Elgar etal.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
667
Child & Youth Care Forum (2020) 49:661–686
1 3
2007). Cronbach’s alphas in this study for Waves 1–3 were 0.69, 0.66, and 0.66 for positive
parenting and 0.78, 0.78, and 0.81 for negative parenting, respectively.
Parenting Stress
To measure foster parent stress we used the short version of the Nijmeegse Ouderlijke
Stress Index (NOSI-K; De Brock etal. 1992), which is based on the Parenting Stress Index
(PSI; Abidin and Abidin 1990). The NOSI-K consists of 25 items which can be answered
on a 6-point Likert scale ranging from 1 (totally disagree) to 6 (totally agree). A sample
item is: ‘Child is more of a problem than expected’. Internal consistencies of the NOSI-K
have been reported to be high (De Brock etal. 1992; Haskett etal. 2006), and the NOSI-K
has been previously used in studies on foster parents (Maaskant etal. 2016; Murray etal.
2011; Nilsen 2007; Van Andel etal. 2015). In the current study the internal consistency for
all three waves was 0.96.
DataAnalysis
The goal of this study was to examine the change in foster children’s mental health over
time and how this change depends on foster child, care experiences, foster family, and fos-
ter placement characteristics. Multilevel modeling was used to deal with the hierarchical
data structure (i.e., the same children are measured over time, causing mental health scores
within an individual foster child to be correlated) and allows to examine within-person dif-
ferences (Singer etal. 2003). The statistical software R was used for the analyses (R Core
Team 2018). Continuous predictor variables were centered around their mean to allow eas-
ier interpretation of intercept and slope parameters (Enders and Tofighi 2007; Peugh 2010).
Missing data for the second and third wave were approximately 51 and 58%
respectively for the different variables included in our model (MmissingWave2 = 50.85;
MmissingWave3 = 57.95). We performed Little’s MCAR test which indicated that the miss-
ing data were missing completely at random (χ2 (1383) = 1383.12, p = 0.49). We also per-
formed t-test and chi-square tests to compare the foster children who participated in Wave
1 only to the foster children who participated in Wave 1 and Wave 2 and/or Wave 3 on
several variables. T-tests (for age, placement duration, placement history, SDQ, NOSI-
K, APQ) and chi-square tests (gender, kinship vs. non-kinship, reunification, quitting)
revealed two differences between the groups. A plan for reunification was more often made
for foster children participating in Wave 1 but not in Wave 2, than for foster children partic-
ipating in Wave 1 and Wave 2 (χ2 (1) = 7.52, p = 0.006). Also, a plan for reunification was
more often made for foster children participating in Wave 1 and 2 but not in Wave 3 than
for foster children participating in all Waves (χ2 (1) = 12.90, p < 0.001). Also, foster parents
participating in Wave 1 and 2, but not in Wave 3 were more likely to think about quitting
foster care than foster parents participating in all Waves (χ2 (1) = 4.03, p = 0.045). For t he
other variables, we found no differences between those who did and those who did not drop
out between waves.
Conventional methods to handle missing data (e.g., pairwise or listwise deletion) are
wasteful and may lead to biased or even false results due to a loss of power (Graham 2009;
Rubin 1987; Schafer and Graham 2002), and therefore we used multiple imputation to ade-
quately deal with missing data (Van Ginkel etal. 2019). Multiple imputation is less biased
especially for larger percentages of missing data because wider confidence intervals are
generated for variables with more missing data, avoiding the risk of false positives (Graham
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
668
Child & Youth Care Forum (2020) 49:661–686
1 3
2009). Given the hierarchical structure of our data, we first tried multilevel multiple impu-
tation with the mice and pan packages. However, this resulted in estimates far outside the
expected range and autocorrelation function (ACF) plots (Azur etal. 2011) revealed that
imputations did not converge (Grund etal. 2016). We therefore continued with single level
multiple imputation in the mice package. Missing data were imputed 100 times, with 100
iterations for each imputation. We used predictive mean matching (PMM) as an imputa-
tion method. PMM predicts the missing values and subsequently selects observed values
which are used to replace the missing values (Heymans and Eekhout 2019). Autocorrela-
tion function (ACF) plots revealed that all imputations converged. In addition, the correla-
tions between variables were approximately the same in the imputed datasets compared to
the non-imputed dataset (see Table1).
The mice and mitml packages in R were used to fit a (pooled) multilevel model to our
multiple imputed dataset and to pool the results (Groothuis-Oudshoorn and Van Buuren
2011; Grund et al. 2016). Using the pooled data, we first tested three consecutive mod-
els that increased in complexity. First, we tested the unconditional means model (Model
1) with and without quadratic time effect to compute the intraclass correlation (ICC) and
decompose the variance within and between persons. We then added time as predictor and
tested the unconditional growth model – random intercepts only (Model 2), and the uncon-
ditional growth model – random intercepts and slopes (Model 3). These unconditional
multilevel models show whether there is systematic variation in foster children’s mental
health outcomes worth exploring, and where that variation resides (within or between sub-
jects). In the fourth, fifth, and sixth model, we successively added the covariates (e.g., age
and gender) and child’s care experiences (block 1), and foster family and foster placement
(block 2) characteristics. Additionally, these factors were controlled for in the second and
third step (foster family and foster placement characteristics respectively). Likelihood ratio
tests were used to evaluate whether model fit improved (Grund et al. 2016). Significant
covariates or predictors were kept in the model when testing subsequent models, resulting
in a final parsimonious model (Model 7).
Results
An overview of the descriptive statistics and correlations is presented in Table1. Foster
children’s mean total behavior problems, the sum of their internalizing and externalizing
problems, (MWave1 = 12.57, SDWa ve1 = 6.94; MWa ve2 = 12.84, SDWav e2 = 7.06; MWav e3 = 12.11,
SDWave 3 = 6.96), fell within the borderline range following the Dutch norm cut-off scores
(Goedhart et al. 2003). With regard to the total behavior problems, 42.4%, 42.0%, and
51.1% of the scores fell within the normal range (range 0–10) for the three consecutive
waves, whereas 12.8%, 14.5%, and 15.4% fell within the borderline range (range 11–13),
and 44.8%, 43.5%, and 33.5% within the clinical range (range 14–40). Correlations for
most variables were in the expected direction and significant. It was found that age was
positively related to duration of the placement, and higher levels of internalizing and
externalizing behaviors corresponded to higher levels of parental stress. We also found a
negative relation between positive parenting and negative parenting. Furthermore, the con-
structs that were measured over time (parenting, foster parent’s stress, and internalizing,
externalizing and prosocial behavior) showed strong positive correlations over time. We
did not find any significant correlations for placement history.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
669
Child & Youth Care Forum (2020) 49:661–686
1 3
Table 1 Mean (SD) and Pearson correlations between predictor and outcome variables
M
(SD)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
1. 10.90
(3.81)
0.058 .300** 0.086 − .330** .464** 0.024 .117* − .185** − 0.043 − .385** .509** 0.033 .146* − .118* − 0.016 − .425** .532** 0.053 0.110 − .172** − 0.104
2. − 0.01
(0.82)
0.058 0.068 .162** − 0.076 0.057 .149** − 0.031 .115* − 0.063 − 0.004 0.016 .198** 0.005 .120* − 0.096 − 0.015 0.012 0.088 − 0.031 0.041 − .137*
3. 58.98
(50.61)
.296** 0.076 − 0.068 0.026 .176** − 0.015 − 0.058 − 0.038 .105* 0.032 .151* 0.021 − 0.053 − 0.058 .118* 0.019 .189** 0.005 − 0.014 − 0.050 0.041
4. 1.20
(1.55)
0.087 .163**− 0.079 − 0.057 .136** 0.098 0.079 .105* − 0.072 − 0.034 0.113 .172** 0.030 .166** − 0.098 − 0.052 0.069 0.085 0.016 0.078 − 0.075
5. 52.77
(5.79)
− .334**− 0.063 0.042− 0.046 − .401** − .243** − .170** − 0.026 .263** .773** − .312** − .209** − 0.108 − 0.004 .180** .744** − .376** − .222** − 0.110 0.024 .161**
6. 33.70
(6.02)
.451** 0.040 .162** .115* − .397** .304** 0.095 0.087 − .138** − .386** .769** .321** 0.069 0.099 − .126* − .408** .775** .314** 0.035 − 0.013 − .143*
7. 56.43
(25.14)
0.019 .146**− 0.007 .101*− .238** .312** .479** .570** − .402** − .237** .242** .780** .368** .477** − .357** − .317** .207** .750** .368** .430** − .345**
8. 5.19
(3.79)
.107*− 0.027− 0.047 0.074 − .166** 0.088 .475** .382** − .345** − .189** 0.072 .365** .742** .346** − .362** − .241** 0.073 .410** .766** .357** − .292**
9. 7.38
(4.54)
− .187** .124*− 0.023 .106* − 0.029 .106* .572** .383** − .279** − 0.001 0.013 .488** .299** .823** − .276** − 0.046 0.015 .465** .240** .765** − .231**
10. 7.08
(2.31)
− 0.037 − 0.070 .107*− 0.069.263** − .139** − .402** − .342**− .281** .224** − .135*− .370** − .298**− .267** .764** .296** − .135* − .336** − .321**− .197** .686**
11. 52.60
(5.67)
− .347**− 0.024 0.016 0.023.785** − .377** − .241** − .176*− 0.030 .216** − .412** − .233** − .149*− 0.021 .221** .807** − .414** − .237** − 0.120 − 0.020 .193**
12. 33.87
(5.76)
.499**− 0.044 .200** 0.021− .281** .778** .250** 0.057 0.050 − 0.134 − .389** .337** 0.077 0.092 − .137* − .427** .812** .276** 0.020 − 0.034 − .144*
13. 57.64
(26.97)
0.009 .136* 0.019 .159*− .194** .308** .789** .339**.517** − .320** − .238** .328** .428**.572** − .418** − .333** .277** .807** .357**.497** − .413**
14. 5.26
(4.00)
0.126 0.005− 0.031 0.062− 0.077 0.051 .363** .767**.292** − .284**− 0.130 0.068 .419** .379** − .393**− .203** 0.065 .462** .796**.412** − .354**
15. 7.61
(4.51)
− 0.092 0.091 0.010 .199**− 0.013 .144* .508** .329**.831** − .228** − 0.018 0.130 .611** .377** − .346** − 0.105 .133* .516** .282**.845** − .296**
16. 7.06
(2.21)
− 0.003 − 0.041 0.086− 0.0710.129 − 0.103 − .334** − .362**− .257** .760** .188** − 0.131 − .396** − .399**− .325** .298** − .154* − .417** − .417**− .302** .778**
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
670
Child & Youth Care Forum (2020) 49:661–686
1 3
Table 1 (continued)
M
(SD)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
17. 52.53
(6.46)
− .360** 0.032 0.035 0.010.778** − .407** − .364** − .284**− 0.114 .339** .837** − .385** − .362** − .240**− 0.094 .290** − .481** − .343** − .169*− 0.071 .300**
18. 33.54
(5.90)
.429**− 0.099 0.136− 0.029 − .373** .790** .195** 0.050 0.068 − 0.147 − .382** .810** .214** 0.071 .163* − 0.130 − .467** .259** 0.026 0.028 − .183**
19. 56.33
(26.01)
0.007 0.034 0.051 0.028 − .236** .318** .770** .404** .493** − .344** − .270** .248** .813** .483** .521** − .379** − .379** .238** .451** .550** − .439**
20. 4.99
(3.84)
0.125 − 0.019 0.044− 0.026 − 0.131 − 0.001 .410** .784** .295**− .310** − .170* 0.031 .359** .827** .289** − .393** − .206** 0.009 .478** .396** − .401**
21. 7.12
(4.43)
− 0.128 0.026 0.024 0.145 − 0.020 0.093 .462** .358** .800** − .217** − 0.048 0.010 .518** .391** .860** − .278** − 0.116 0.088 .575** .414** − .333**
22. 7.10
(2.36)
− 0.116 − 0.082 − 0.040− 0.044 .199** − 0.122 − .358** − .259** − .274** .726** .173* − 0.092 − .379** − .331** − .286** .795** .334** − .173* − .439** − .398** − .352**
1 = age, 2 = SES, 3 = Duration placement, 4 = Placement history, 5 = T1 Positive parenting, 6 = T1 Negative parenting, 7 = T1 Foster parents’ stress, 8 = T1 Internalizing
problems, 9 = T1 Externalizing problems, 10 = T1 Prosocial behavior, 11 = T2 Positive parenting, 12 = T2 Negative parenting, 13 = T2 Foster parents’ stress, 14 = T2 Inter-
nalizing problems, 15 = T2 Externalizing problems, 16 = T2 Prosocial behavior, 17 = T3 Positive parenting, 18 = T3 Negative parenting, 19 = T3 = Foster parents’ stress, 20
= T3 Internalizing behaviors, 21 = T3 Externalizing behaviors, 22 = T3 Prosocial behavior
Under the diagonal: correlations for non-imputed data. Above the diagonal: correlations for imputed data. Mean (SD) are given for the original dataset only
*p < .05; **p < .01
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
671
Child & Youth Care Forum (2020) 49:661–686
1 3
Prosocial Behavior
The results of the multilevel models on prosocial behavior are presented in Table2. Based
on the first model, we estimated the intra-class correlation (i.e., the correlation between
measurements of the same child) being 0.74. This means that approximately three-quarters
of the total variance in prosocial behavior pertains to differences between foster children
prosocial behavior scores. This implies that large differences exist between children in
their average prosocial behavior scores (averaged across time) compared to the differences
in prosocial behavior scores within a child (variation over time within a child). Model 2
showed no increasing or decreasing trend in prosocial behavior over time. In other words:
there was no effect of time. The likelihood ratio test (LRT) indicated that Model 2 did not
fit the data better than Model 1 (χ2 (df = 1) = 0.01, p = 0.92). In Model 3 we tested whether
foster children differ in their intercepts and slopes. There was a significant improvement
when comparing model 3 to model 2 (χ2 (df = 2) = 6.10, p = 0.002), meaning that children
differed in the rate of change of prosocial behavior. Model 4 did not fit the data better than
model 3 (χ2 (df = 2) = 0.67, p = 0.51), and showed that there were no main effects of the
covariates age and gender, so these were removed from the model. In the fifth model, we
added the block 1 predictors (e.g., child’s care experiences characteristics). Neither place-
ment history nor the duration of the foster placement was significantly related to foster
children’s prosocial behavior. Model 5 did not fit the data significantly better than model
3 (χ2 (df = 2) = 2.30, p = 0.10). In the sixth model, we added the foster family and foster
placement characteristics, which led to a significant improvement compared to model 3
(χ2 (df = 7) = 12.80, p < 0.001). Type of placement was a significant predictor of prosocial
behavior, with foster children in kinship foster families showing more prosocial behavior
than foster children in non-kinship foster families (b = -0.43, p < 0.01). In addition, foster
parent stress predicted foster children’s prosocial behavior, with high foster parent stress
related to lower prosocial behaviors (b = -0.02, p < 0.001). Lastly, positive foster parenting
was related to higher levels of prosocial behavior (b = 0.06, p < 0.001). The final parsimoni-
ous model, Model 7, with only the significant predictors included, is presented in Table2.
Internalizing Behavior Problems
The results for the multilevel models on internalizing behaviors are displayed in Table3.
Based on the first model, the intra-class correlation was estimated 0.77, indicating that
77% of the total variance (i.e., differences) in internalizing behaviors pertains to differ-
ences between foster children, meaning that there are large differences between foster chil-
dren’s internalizing behavior levels. Model 2 showed that there was no increase or decrease
in internalizing behavior over time and Model 2 did not fit the data better than Model
1 (χ2 (df = 1) = 0.01, p = 0.93). In Model 3 we tested whether foster children differed in
their intercept and slopes, which appeared not to be the case (χ2 (df = 2) = 0.59, p = 0.55).
Although Model 3 did not seem to fit the data better than Model 2, we decided to continue
with Model 3 because keeping a (non-significant) random slope in the model allows for
extra modeling flexibility while not harming the estimates for the other model parameters.
In Model 4, the covariates age and gender were added. Age significantly predicted inter-
nalizing behaviors such that older children showed more internalizing problems (b = 0.12,
p = 0.01). Age was therefore kept in the model. Model 4 also fitted the data significantly
better than Model 3, (χ2 (df = 2) = 4.48, p = 0.01). In Model 5, we found that neither of the
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
672
Child & Youth Care Forum (2020) 49:661–686
1 3
Table 2 Results of the (pooled) multilevel models for prosocial behaviors
Param-
eter
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Coef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
t
Intercept 7.11
(0.11)
64.14*** 7.10
(0.14)
50.03*** 7.10
(0.15)
48.93*** 7.06
(0.18)
39.55*** 7.13
(0.15)
48.20*** 6.99
(0.40)
17.36*** 7.39
(0.20)
36.30***
Time 0.01
(0.06)
0.10 0.01
(0.06)
0.10 0.01
(0.06)
0.01 0.01
(0.06)
0.10 0.04
(0.06)
0.71 0.04
(0.06)
0.71
Covari-
ates
Age at
T1
− 0.03
(0.03)
− 1.13
Gender 0.09
(0.23)
0.37
Block 1
Place-
ment
his-
tory
− 0.01
(0.01)
− 1.06
Place-
ment
dura-
tion
0.00
(0.00)
1.80
Block 2
Type of
foster
fam-
ily
− 0.56
(0.23)
− 2.47* − 0.43
(0.22)
− 2.47*
Reunifi-
cation
0.57
(0.41)
1.39
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
673
Child & Youth Care Forum (2020) 49:661–686
1 3
Table 2 (continued)
Param-
eter
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Coef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
t
Quitting − 0.11
(0.26)
− 0.42
SES − 0.10
(0.12)
− 0.82
Foster
parent
stress
− 0.02
(0.00)
− 6.51*** − 0.02
(0.00)
− 6.89***
Positive
par-
enting
0.06
(0.02)
3.72*** 0.06
(0.02)
4.03***
Nega-
tive
par-
enting
− 0.00
(0.02)
0.05
Var(υ0 j) 3.92 3.92 5.10 5.12 4.98 3.82 3.87
Var(εij) 1.37 1.37 1.04 1.04 1.04 1.06 1.07
Gender coded as 0 = boy, 1 = girl. Type of foster family coded as 0 = kinship foster care, 1 = non-kinship foster care. Reunification coded as 0 = no planning for reunification,
1 = planning for reunification. Quitting coded as 0 = foster parent does not think about quitting foster care, 1 = foster parent does think about quitting foster care
*p < .05; **p < .01; ***p < .001
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
674
Child & Youth Care Forum (2020) 49:661–686
1 3
Table 3 Results of the (pooled) multilevel models for internalizing behaviors
Parameter Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Coef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
t
Intercept 5.20
(0.19)
27.21*** 5.22
(0.24)
22.02*** 5.22
(0.23)
22.62*** 4.98
(0.29)
17.42*** 5.20
(0.24)
22.03*** 6.05
(0.66)
9.19* 5.26
(0.22)
24.31***
Time − 0.01
(0.10)
− 0.09 − 0.01
(0.10)
− 0.10 − 0.01
(0.10)
− 0.10 − 0.01
(0.10)
− 0.10 − 0.02
(0.10)
− 0.19 − 0.03
(0.10)
− 0.28
Covari-
ates
Age at
T1
0.12
(0.05)
2.52* 0.15
(0.05)
3.04** 0.15
(0.05)
2.82** 0.11
(0.04)
2.65**
Gender 0.53
(0.36)
1.45
Block 1
Place-
ment
his-
tory
0.07
(0.14)
0.49
Place-
ment
dura-
tion
− 0.01
(0.00)
− 1.80
Block 2
Type of
foster
family
0.10
(0.36)
0.26
Reunifi-
cation
− 0.50
(0.67)
− 0.74
Quitting − 0.55
(0.45)
− 1.23
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
675
Child & Youth Care Forum (2020) 49:661–686
1 3
Table 3 (continued)
Parameter Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Coef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
t
SES − 0.38
(0.20)
− 1.87
Foster
parent
stress
0.06
(0.01)
8.67*** 0.05
(0.01)
9.37***
Positive
par-
enting
− 0.00
(0.03)
− 0.16
Nega-
tive
par-
enting
− 0.04
(0.03)
− 1.24
Var(υ0 j) 11.57 11.57 10.47 10.15 9.98 7.49 7.72
Var(εij) 3.54 3.53 3.46 3.5 3.46 3.25 3.24
Gender coded as 0 = boy, 1 = girl. Type of foster family coded as 0 = kinship foster care, 1 = non-kinship foster care. Reunification coded as 0 = no planning for reunification,
1 = planning for reunification. Quitting coded as 0 = foster parent does not think about quitting foster care, 1 = foster parent does think about quitting foster care
*p < .05; **p < .01; ***p < .001
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
676
Child & Youth Care Forum (2020) 49:661–686
1 3
two child’s care experience characteristics (i.e., placement history and placement duration)
significantly predicted internalizing problems and that Model 5 did not fit the data signifi-
cantly better than Model 4, (χ2 (df = 1) = 1.51, p = 0.22). Child’s care experience character-
istics were therefore removed from the model. Model 6, with the foster family and foster
placement characteristics included, resulted in a significant model improvement compared
to Model 4 (χ2 (df = 6) = 16.00, p < 0.001). Foster parent stress was a significant predictor
(b = 0.06, p < 0.001), indicating that lower parenting stress was related to fewer internal-
izing behaviors. The results for the final parsimonious Model 7, with only the significant
predictors included, can be found in Table3.
Externalizing Behavior Problems
The last set of multilevel models was run for externalizing behaviors. The results are pre-
sented in Table4. Model 1 indicated that more than 80% (ICC = 0.81) of the total variance
in externalizing behaviors pertains to differences between foster children, indicating that
large differences exist between foster children’s average externalizing behavior scores com-
pared to the differences in externalizing scores within a child. Model 2 showed no increas-
ing or decreasing trends in externalizing behaviors over time (χ2 (df = 1) = 1.90, p = 0.17).
Model 3 indicated a significant improvement compared to Model 2 (χ2 (df = 2) = 6.74,
p = 0.001), indicating difference in the rate of change of externalizing behavior. In Model 4,
we added the covariates age and gender of which age appeared to be significant (b = -0.19,
p = 0.001), with older children showing fewer externalizing behavior problems. Age was
therefore retained in the model. Model 4 showed a significant improvement compared to
Model 3 (χ2 (df = 2) = 5.51, p < 0.01). In Model 5, we added placement history and place-
ment duration, but neither was a significant predictor and Model 5 did not fit the data bet-
ter than Model 4 (χ2 (df = 1) = 2.65, p = 0.10). Model 6 included foster family and foster
placement characteristics and resulted in a better fit than Model 4, χ2 (df = 6) = 28.00,
p < 0.001. It yielded a positive effect of foster parent stress, with higher stress correspond-
ingto higher levels of externalizing behavior problems (b = 0.08, p < 0.001). The final parsi-
monious model, Model7, included the significant predictors (age, foster parent stress) and
is presented in Table4. Model 7 did not fit the data better than Model 6 (χ2 (df = 6) = 1.06,
p = 0.38).
Discussion
The aim of this study was to examine change in foster children’s mental health over time
and how this change depends on characteristics related to the foster child, the child’s care
experiences, foster family, and foster placement. Foster children’s mental health is an
important predictor of foster placement success (Konijn etal. 2019). We found that levels
of mental health were stable over time, meaning that, on average, there were no increas-
ing or decreasing trends in prosocial, internalizing, and externalizing behaviors during the
one year study period. This is in line with the meta-analysis of Goemans etal. (2015) that
analyzed over thirty longitudinal studies on foster children’s behavioral development and
found no increases or decreases in mental health problems. Furthermore, our finding is
in line with the recently published five-year longitudinal study of Hiller and Clair (2018).
Hiller and Clair (2018) used growth mixture modeling, a method to identify clusters of fos-
ter children showing similar patterns of mental health over time. They found that for most
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
677
Child & Youth Care Forum (2020) 49:661–686
1 3
Table 4 Results of the (pooled) multilevel models for externalizing behaviors
Param-
eter
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Coef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoeffi-
cient (SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
t
Intercept 7.21
(0.21)
33.80*** 7.49
(0.27)
28.26*** 7.49
(0.27)
27.36*** 7.45
(0.33)
22.35*** 7.39
(0.28)
26.64*** 7.08
(0.70)
10.05*** 7.55
(0.23)
32.38***
Time − 0.14
(0.10)
− 1.39 − 0.14
(0.11)
− 1.32 − 0.14
(0.11)
− 1.32 − 0.14
(0.11)
− 1.32 − 0.18
(0.10)
− 1.89 − 0.17
(0.09)
− 1.78
Covari-
ates
Age at
T1
− 0.19
(0.06)
− 3.38** − 0.19
(− 0.06)
− 3.36*** − 0.24
(0.05)
− 4.45*** − 0.21
(0.05)
− 4.53***
Gender 0.09
(0.43)
0.20
Block 1
Place-
ment
his-
tory
0.03
(0.02)
1.70
Place-
ment
dura-
tion
0.00
(0.00)
0.02
Block 2
Type of
foster
fam-
ily
0.49
(0.40)
1.22
Reuni-
fica-
tion
0.47
(0.70)
0.67
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
678
Child & Youth Care Forum (2020) 49:661–686
1 3
Table 4 (continued)
Param-
eter
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Coef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
tCoeffi-
cient (SE)
tCoef-
ficient
(SE)
tCoef-
ficient
(SE)
t
Quit-
ting
− 0.33
(0.48)
− 0.70
SES 0.13
(0.23)
0.56
Foster
par-
ent
stress
0.08
(0.01)
11.93*** 0.08
(0.01)
14.04***
Posi-
tive
par-
ent-
ing
0.061
(0.03)
0.35
Nega-
tive
par-
ent-
ing
0.05
(0.03)
1.55
Var(υ0 j) 16.35 16.36 20.65 20.07 19.87 12.58 12.84
Var(εij) 3.87 3.84 2.87 2.87 2.87 2.62 2.62
Gender coded as 0 = boy, 1 = girl. Type of foster family coded as 0 = kinship foster care, 1 = non-kinship foster care. Reunification coded as 0 = no planning for reunification,
1 = planning for reunification. Quitting coded as 0 = foster parent does not think about quitting foster care, 1 = foster parent does think about quitting foster care
*p < .05; **p < .01; ***p < .001
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
679
Child & Youth Care Forum (2020) 49:661–686
1 3
mental health domains the largest trajectories (i.e., the cluster containing most foster chil-
dren) were stable, with no improvement or deterioration over the five-year study period.
Moreover, for each mental health domain, the majority of the children fell into categories
that showed stability over time (Hiller and Clair 2018).
Despite this stability, previous longitudinal studies and our study showed that fos-
ter children show different levels of internalizing, externalizing and prosocial behaviors.
Foster children varied in their levels of mental health, and for prosocial and externalizing
behavior the change over time varied also between foster children. In other words, there
were differences between foster children’s developmental outcomes with respect to proso-
cial and externalizing behavior. Heterogeneity in foster children’s development was also
shown in a 7-to-9-year longitudinal study by Tarren-Sweeney (2017). Although foster chil-
dren’s mean scores on average did not change, different groups of foster children showing
similar patterns of mental health difficulties were distinguished. For example, 60% of the
foster children manifested either sustained mental health or meaningful improvement in
their mental health. The study of Hiller and Clair (2018) also showed that although most
children showed stable trajectories of mental health, there were also ‘latent trajectories’
(symptoms started in the normal range and significantly increased to the abnormal range).
These findings illustrate that although foster children’s development on average seems to
be stable, foster children are a diverse group. Besides studying general trends in foster chil-
dren’s development, it is important to take the heterogeneity of foster children’s develop-
mental trajectories into account when studying their development (Tarren-Sweeney 2017).
Given this heterogeneity the current study examined what accounts for different
developmental outcomes by studying predictors of mental health outcomes. We simul-
taneously included a broad range of characteristics related to the foster child, child’s
care experiences, foster family, and foster placement in the analyses. Foster parent stress
was consistently found to be the strongest predictor of foster children’s mental health
outcomes. Increased stress as reported by the foster parents was related to lower lev-
els of foster parent reported prosocial behavior and higher levels of internalizing and
externalizing problems. It is important to note that foster parents were the sole inform-
ants in this study and reported both on their own stress and the foster child’s mental
health. This potentially resulted in an overestimation of the association between vari-
ables of interest (Keijsers etal. 2012; O’Connor 2002). Nonetheless, our study suggests
that foster parent stress and foster children’s mental health are correlated, which is in
line with previous research (e.g., Kelley etal. 2011; McSherry etal. 2018; Murray etal.
2011). However, only a few longitudinal studies have examined the directionality of the
effects (Gabler etal. 2018; Goemans et al. 2018a; Lohaus et al. 2018). These studies
found significant weak to moderate correlations between stress of parents at the first
time point and mental health of foster children at a later time point. Two of these studies
(Goemans etal. 2018a; Lohaus etal. 2018) tested this relation in a multivariate model
using structural equation modeling, and found no significant prospective pathways from
foster parent stress to foster children’s behavior problems, meaning that foster parent
stress did not predict foster children’s mental health. As Lohaus etal. (2018) suggested,
identifying prospective pathways might be difficult if most variance of later assessments
is already explained by the previous assessments, which is shown by the high stability
across assessments. Moreover, both studies showed weak to moderate concurrent rela-
tions between foster parent stress and foster children’s behavior problems. Nevertheless,
foster parent stress is an important factor to take into account when looking at foster
children’s mental health. Although the predictive value of foster parent stress on foster
children’s mental health might be small, the two factors are correlated. This means that
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
680
Child & Youth Care Forum (2020) 49:661–686
1 3
in case of studying or identifying either foster parent stress or foster children’s mental
health, researchers and practitioners should be aware of possible co-occurrence and pro-
vide social support when necessary (Cooley etal. 2019).
We also found that the type of placement and positive parenting were significant pre-
dictors of foster children’s prosocial behavior. Foster children in kinship foster families
showed more prosocial behavior than children in non-kinship families. This finding is in
line with the positive findings for kinship families that have been shown by others (see
Winokur etal. 2018 for a review and meta-analysis). The finding that positive parent-
ing is positively related to prosocial behavior might be explained by a social learning
mechanism. Foster children may learn to act prosocial by observing the positive and
prosocial behaviors as modelled by their foster parents (Bandura and Walters 1977).
The finding that positive parenting is predictive of foster children’s prosocial behavior
is hopeful because it could indicate that foster parents can boost their foster children’s
development through positive parenting. This is a reassuring finding and in line with
the positive effects of intervention programs on parent outcomes and child problems
(Schoemaker etal. 2019).
Regarding internalizing and externalizing behaviors, we found no other predictors
than foster parent stress besides the age of the children. The effect of age differed for
internalizing and externalizing behaviors. Older children showed more internalizing
behaviors and fewer externalizing problems compared to younger children. Although
this result is in line with the broader child mental health prevalence literature (e.g.,
Bongers etal. 2003), the latter effect is still surprising because most studies on foster
children suggest that older foster children show more internalizing as well as external-
izing problems (Armsden etal. 2000; Dubowitz etal. 1993; Heflinger etal. 2000; Maas-
kant etal. 2014). However, not all studies found the same effect of age on externaliz-
ing behaviors. For example, in a longitudinal study among adolescents aged 13 to 16,
McWey et al. (2010) found that older adolescents demonstrated lower levels of both
externalizing and internalizing problems. Moreover, Vanderfaeillie etal. (2013) did not
find any age effect at all. Possible explanations for the different findings could be the
focus on a different age range (McWey et al. 2010) or the inclusion of other predic-
tors confounded with age such as age of entry into care (Tarren-Sweeney 2008). One
last explanation for the negative effect of age on externalizing behaviors in our sample
might be related to the characteristics of our sample. Our sample consisted of a group of
foster children in relatively stable and long-term placements. On average, the foster chil-
dren in this study resided for almost five years in their current foster placement. Because
especially age and externalizing behaviors (Konijn etal. 2019; Oosterman etal. 2007),
are related to placement breakdown, it could be that the group of older foster children
showing externalizing behavior was underrepresented in our sample.
The characteristics of our sample might also explain why we did not find an effect of
placement history on foster children’s mental health. Several previous studies suggest that
placement history (i.e., the number of previous placements) is one of the strongest predic-
tors of foster children’s mental health (Newton etal. 2000; Rubin etal. 2007). However,
our marker of placement stability did not include how long the child was in care, because
this information was often unknown to foster parents. Although we did not find a signifi-
cant correlation between placement duration and placement history in our sample, it could
be that children who are longer in care experienced more changes of placement. Our result
that placement history is not related to children’s mental health outcomes should therefore
be interpreted with caution because it might apply only to foster children in relatively sta-
ble and long-term placement and without a volatile placement history.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
681
Child & Youth Care Forum (2020) 49:661–686
1 3
Limitations andDirections forFuture Research
This study aimed to provide a better insight in which characteristics are related to foster
children’s mental health, using a three-wave longitudinal design. In the current study foster
parents were the sole informants. Future research should include multiple informants or
sources of information. This way, same method variance, which might result in an overes-
timation of the association between the variables of interest, can be prevented (Brannick
etal. 2010). Furthermore, it is necessary to consider other measures than self-reports. For
example, foster parents’ cortisol levels could be used as a biological measure of stress. In
addition, foster children’s mental health could be measured by observational measures or
teacher or self-reports (Boada 2007; McAuley and Trew 2000; Shore etal. 2002). Includ-
ing multiple informants and different measures of the same construct would allow for a
replication and validation of previous study findings.
The current study covered foster children’s development over a one-year period, meas-
ured during three waves separated by six month intervals. We had a considerable amount
of missing data due to attrition between waves. Our efforts to prevent attrition, for example
by using incentives and sending several reminders, unfortunately had only limited effect.
Although we compared our final sample with the group that dropped out after Wave I on
several important characteristics and found only two significant differences, we cannot
exclude the possibility that there are important differences between those who continued
to participate and those who dropped out on variables that were not measured. Attrition
is a common problem within longitudinal research, and longitudinal studies on foster care
are not an exception (Jackson etal. 2012). Strategies for longitudinal research with foster
children as described by Jackson etal. (2012) are helpful to prevent attrition in longitudinal
designs. Example strategies mentioned by Jackson etal. (2012) are ensuring positive data
collection experiences at one time point to prevent attrition for the next time point. If even-
tually, however, attrition remains high, researchers should be transparent in reporting their
missing data, and should apply modern methods to handle missing data (Graham 2009),
such as multiple imputation or FIML estimation.
A last point for future research is to more thoroughly examine the developmental pro-
cesses and dynamic systems (i.e., interactions) of foster children and foster care. Collecting
more intensive, longitudinal data with many measurements over time could provide new
insights. For example, such an approach might considerably improve the opportunity to
study inter-individual variability in intra-individual patterns of change (or development)
over time.
Conclusions
The aim of this study was to establish what accounts for the differences in foster children’s
mental health outcomes. Knowledge regarding foster children’s mental health outcomes
and its predictors could provide valuable information to inform the design of targeted inter-
ventions and support services for foster children and foster families. The strength of our
study was the use of a multivariate model in which characteristics of the foster child, the
child’s care experiences, foster family and foster placement were included as predictors of
foster children’s mental health. Among these different characteristics, foster parent stress
appeared to be the most important predictor of foster children’s mental health. In our study,
a considerable number of foster parents showed ‘above average’ parenting stress levels
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
682
Child & Youth Care Forum (2020) 49:661–686
1 3
(approximately 40%). This is worrisome given the relation we found with foster children’s
mental health. Additionally, we also know that foster parent stress may negatively impact
their motivation to continue fostering and may lead to foster parent burnout and placement
disruption (Leathers et al. 2019). Therefore, it seems important to consider foster parent
stress in screening and interventions, as a possible point of attention in creating condi-
tions conducive to foster children’s mental health. In addition, targeting the source of stress
might be helpful in this effort. Previous research showed that foster children’s challeng-
ing behaviors are perceived as very stressful by foster parents and contribute most to their
stress levels (McKeough etal. 2017). A recent meta-analysis shows promising effects of
foster care interventions on parenting stress (g = 0.60) and foster children’s behavior prob-
lems (g = 0.53) (Schoemaker etal. 2019). Our study pointing out foster parent stress as a
key factor related to foster children’s mental health, the promising findings of the meta-
analysis of Schoemaker etal. (2019) regarding the efficacy of interventions in reducing fos-
ter parent stress, and the fact that foster parents themselves express a desire for additional
training (McKeough etal. 2017) should provide a clear signal to policymakers and profes-
sionals to improve foster parent support and training for better placement outcomes.
Acknowledgements None.
Compliance with Ethical Standards
Conict of interest The authors declare that they have no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.
References
Abidin, R. R., & Abidin, R. R. (1990). Parenting Stress Index (PSI). Charlottesville, VA: Pediatric Psychol-
ogy Press.
Achenbach, T. M., Becker, A., Döpfner, M., Heiervang, E., Roessner, V., Steinhausen, H., et al. (2008).
Multicultural assessment of child and adolescent psychopathology with ASEBA and SDQ instruments:
Research findings, applications, and future directions. Journal of Child Psychology and Psychiatry,
49(3), 251–275.
Ahmad, A., Qahar, J., Siddiq, A., Majeed, A., Rasheed, J., Jabar, F., etal. (2005). A 2-year follow-up of
orphans’ competence, socioemotional problems and post-traumatic stress symptoms in traditional fos-
ter care and orphanages in Iraqi Kurdistan. Child: Care, Health and Development, 31(2), 203–215.
Ainsworth, F., & Hansen, P. (2014). Family foster care: Can it survive the evidence? Children Australia,
39(2), 87–92.
Andersen, A., Krølner, R., Currie, C., Dallago, L., Due, P., Richter, M., etal. (2008). High agreement on
family affluence between children’s and parents’ reports: International study of 11-year-old children.
Journal of Epidemiology & Community Health, 62(12), 1092–1094.
Armsden, G., Pecora, P. J., Payne, V. H., & Szatkiewicz, J. P. (2000). Children placed in long-term foster
care: An intake profile using the Child Behavior Checklist/4-18. Journal of Emotional and Behavioral
Disorders, 8(1), 49–64.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
683
Child & Youth Care Forum (2020) 49:661–686
1 3
Azur, M. J., Stuart, E. A., Frangakis, C., & Leaf, P. J. (2011). Multiple imputation by chained equations:
What is it and how does it work? International Journal of Methods in Psychiatric Research, 20(1),
40–49.
Bandura, A., & Walters, R. H. (1977). Social learning theory (Vol. 1). Englewood Cliffs, NJ: Prentice-Hall.
Barber, J., & Delfabbro, P. (2005). Children’s adjustment to long-term foster care. Children and Youth Ser-
vices Review, 27(3), 329–340.
Boada, C. M. (2007). Kinship foster care: A study from the perspective of the caregivers, the children and
the child welfare workers. Psychology in Spain, 11(1), 42–52.
Boyce, W., Torsheim, T., Currie, C., & Zambon, A. (2006). The family affluence scale as a measure of
national wealth: Validation of an adolescent self-report measure. Social Indicators Research, 78(3),
473–487.
Bowlby, J. (1969). Attachment and loss: Attachement (Vol. I). London: The Tavistock Institute of Human
Relations.
Brannick, M. T., Chan, D., Conway, J. M., Lance, C. E., & Spector, P. E. (2010). What is method variance
and how can we cope with it? A panel discussion. Organizational Research Methods, 13(3), 407–420.
Chamberlain, P., Price, J., Leve, L. D., Laurent, H., Landsverk, J. A., & Reid, J. B. (2008). Prevention of
behavior problems for children in foster care: Outcomes and mediation effects. Prevention Science,
9(1), 17–27.
Cicchetti, D., Toth, S. L., & Maughan, A. (2000). An ecological-transactional model of child maltreatment.
In A. J. Sameroff, M. Lewis & S. M. Miller (Eds.), Handbook of developmental psychopathology (pp.
689–722). Boston, MA: Springer.
Clausen, J. M., Landsverk, J., Ganger, W., Chadwick, D., & Litrownik, A. (1998). Mental health problems
of children in foster care. Journal of Child and Family Studies, 7(3), 283–296.
Cooley, M. E., Thompson, H. M., & Newell, E. (2019). Examining the influence of social support on the
relationship between child behavior problems and foster parent satisfaction and challenges. Child &
Youth Care Forum, 48(3), 289–303.
Cuddeback, G. S. (2004). Kinship family foster care: A methodological and substantive synthesis of
research. Children and Youth Services Review, 26(7), 623–639. https ://doi.org/10.1016/J.CHILD
YOUTH .2004.01.014.
Currie, C. E., Elton, R. A., Todd, J., & Platt, S. (1997). Indicators of socioeconomic status for adolescents:
The WHO Health Behaviour in School-aged Children Survey. Health Education Research, 12(3),
385–397.
Dadds, M. R., Maujean, A., & Fraser, J. A. (2003). Parenting and conduct problems in children: Australian
data and psychometric properties of the Alabama Parenting Questionnaire. Australian Psychologist,
38(3), 238–241.
De Brock, A., Vermulst, A. A., Gerris, J. R. M., & Abidin, R. R. (1992). NOSI: Nijmeegse ouderlijke stress
index. Lisse: Swets En Zeitlinger.
Dixon, J. (2008). Young people leaving care: Health, well-being and outcomes. Child & Family Social
Work, 13(2), 207–217.
Dozier, M., Kaufman, J., Kobak, R., O’Connor, T. G., Sagi-Schwartz, A., Scott, S., etal. (2014). Consensus
statement on group care for children and adolescents: A statement of policy of the American Orthopsy-
chiatric Association. American Journal of Orthopsychiatry, 84(3), 219.
Dubowitz, H., Zuravin, S., Starr, R. H., Feigelman, S., & Harrington, D. (1993). Behavior problems of chil-
dren in kinship care. Journal of Developmental and Behavioral Pediatrics, 14(6), 386–393.
Elgar, F. J., Waschbusch, D. A., Dadds, M. R., & Sigvaldason, N. (2007). Development and validation of
a short form of the Alabama Parenting Questionnaire. Journal of Child and Family Studies, 16(2),
243–259.
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A
new look at an old issue. Psychological Methods, 12(2), 121.
Fanshel, D., & Shinn, E. B. (1978). Children in foster care: A longitudinal investigation. New York: Colum-
bia University Press.
Fernandez, E. (2009). Children’s wellbeing in care: Evidence from a longitudinal study of outcomes. Chil-
dren and Youth Services Review, 31(10), 1092–1100.
Frank, G. (1980). Treatment needs of children in foster care. American Journal of Orthopsychiatry, 50(2),
256.
Frick, P. J. (1991). The Alabama parenting questionnaire. Unpublished Rating Scale, University of Alabama.
Gabler, S., Kungl, M., Bovenschen, I., Lang, K., Zimmermann, J., Nowacki, K., etal. (2018). Predictors of
foster parents’ stress and associations to sensitivity in the first year after placement. Child Abuse &
Neglect, 79, 325–338.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
684
Child & Youth Care Forum (2020) 49:661–686
1 3
Goemans, A., Van Geel, M., Van Beem, M., & Vedder, P. (2016a). Developmental outcomes of foster chil-
dren: A meta-analytic comparison with children from the general population and children at risk who
remained at home. Child Maltreatment, 21(3), 198–217.
Goemans, A., Van Geel, M., & Vedder, P. (2015). Over three decades of longitudinal research on the devel-
opment of foster children: A meta-analysis. Child Abuse & Neglect, 42, 121–134.
Goemans, A., Van Geel, M., & Vedder, P. (2016b). Psychosocial functioning in Dutch foster children: The
relationship with child, family, and placement characteristics. Child Abuse & Neglect, 56, 30–43.
Goemans, A., Van Geel, M., & Vedder, P. (2018a). Foster children’s behavioral development and foster par-
ent stress: Testing a transactional model. Journal of Child and Family Studies, 27(3), 990–1001.
Goemans, A., Van Geel, M., Wilderjans, T. F., Van Ginkel, J. R., & Vedder, P. (2018b). Predictors of school
engagement in foster children: A longitudinal study. Children and Youth Services Review, 88, 33–43.
Goodman, A., Lamping, D. L., & Ploubidis, G. B. (2010). When to use broader internalising and externalis-
ing subscales instead of the hypothesised five subscales on the Strengths and Difficulties Question-
naire (SDQ): Data from British parents, teachers and children. Journal of Abnormal Child Psychology,
38(8), 1179–1191.
Goodman, R. (1997). The Strengths and Difficulties Questionnaire: A research note. Journal of Child Psy-
chology and Psychiatry, 38(5), 581–586.
Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychol-
ogy, 60, 549–576.
Groothuis-Oudshoorn, K., & Van Buuren, S. (2011). Mice: Multivariate imputation by chained equations in
R. Journal of Statistical Software, 45(3), 1–67.
Grund, S., Lüdtke, O., & Robitzsch, A. (2016). Multiple imputation of multilevel missing data: An introduc-
tion to the R package pan. SAGE Open, 6(4), 2158244016668220.
Haskett, M. E., Ahern, L. S., Ward, C. S., & Allaire, J. C. (2006). Factor structure and validity of the parent-
ing stress index-short form. Journal of Clinical Child & Adolescent Psychology, 35(2), 302–312.
Heflinger, C. A., Simpkins, C. G., & Combs-Orme, T. (2000). Using the CBCL to determine the clinical
status of children in state custody. Children and Youth Services Review, 22(1), 55–73.
Heymans, W. M., & Eekhout, I. (2019). Applied missing data analysis with SPSS and (R)Studio. Retrieved
from https ://bookd own.org/mwhey mans/bookm i/.
Hiller, R. M., & Clair, M. C. S. (2018). The emotional and behavioural symptom trajectories of children in
long-term out-of-home care in an English local authority. Child Abuse & Neglect, 81, 106–117.
Holtan, A., Rønning, J. A., Handegård, B. H., & Sourander, A. (2005). A comparison of mental health prob-
lems in kinship and nonkinship foster care. European Child & Adolescent Psychiatry, 14(4), 200–207.
https ://doi.org/10.1007/s0078 7-005-0445-z.
Jackson, Y., Gabrielli, J., Tunno, A. M., & Hambrick, E. P. (2012). Strategies for longitudinal research with
youth in foster care: A demonstration of methods, barriers, and innovations. Children and Youth Ser-
vices Review, 34(7), 1208–1213.
Kelley, S. J., Whitley, D. M., & Campos, P. E. (2011). Behavior problems in children raised by grandmoth-
ers: The role of caregiver distress, family resources, and the home environment. Children and Youth
Services Review, 33(11), 2138–2145.
Konijn, C., Admiraal, S., Baart, J., van Rooij, F., Stams, G.-J., Colonnesi, C., etal. (2019). Foster care place-
ment instability: A meta-analytic review. Children and Youth Services Review, 96, 483–499.
Lawrence, C. R., Carlson, E. A., & Egeland, B. (2006). The impact of foster care on development. Develop-
ment and Psychopathology, 18(1), 57–76.
Leathers, S. J., Spielfogel, J. E., Geiger, J., Barnett, J., & Voort, B. L. V. (2019). Placement disruption
in foster care: Children’s behavior, foster parent support, and parenting experiences. Child Abuse &
Neglect, 91, 147–159.
Leathers, S. J., Spielfogel, J. E., McMeel, L. S., & Atkins, M. S. (2011). Use of a parent management train-
ing intervention with urban foster parents: A pilot study. Children and Youth Services Review, 33(7),
1270–1279.
Lehmann, S., Havik, O. E., Havik, T., & Heiervang, E. R. (2013). Mental disorders in foster children: A
study of prevalence, comorbidity and risk factors. Child and Adolescent Psychiatry and Mental Health,
7(1), 39.
Li, D., Chng, G. S., & Chu, C. M. (2017). Comparing long-term placement outcomes of residential and
family foster care: A meta-analysis. Trauma, Violence, & Abuse, 1524838017726427.
Lohaus, A., Kerkhoff, D., Chodura, S., Möller, C., Symanzik, T., Rueth, J. E., etal. (2018). Longitudinal
relationships between foster children’s mental health problems and parental stress in foster mothers and
fathers. European Journal of Health Psychology, 25(2), 33–42.
Maaskant, A. M. (2016). Placement breakdown in foster care: Reducing risks by a foster parent training
program? Universiteit van Amsterdam.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
685
Child & Youth Care Forum (2020) 49:661–686
1 3
Maaskant, A. M., van Rooij, F. B., & Hermanns, J. M. A. (2014). Mental health and associated risk fac-
tors of Dutch school aged foster children placed in long-term foster care. Children and Youth Services
Review, 44, 207–216. https ://doi.org/10.1016/J.CHILD YOUTH .2014.06.011.
Maaskant, A. M., van Rooij, F. B., Overbeek, G. J., Oort, F. J., & Hermanns, J. M. A. (2016). Parent training
in foster families with children with behavior problems: Follow-up results from a randomized con-
trolled trial. Children and Youth Services Review, 70, 84–94.
McAuley, C., & Trew, K. (2000). Children’s adjustment over time in foster care: Cross-informant agree-
ment, stability and placement disruption. British Journal of Social Work, 30(1), 91–107.
McKeough, A., Bear, K., Jones, C., Thompson, D., Kelly, P. J., & Campbell, L. E. (2017). Foster carer stress
and satisfaction: An investigation of organisational, psychological and placement factors. Children and
Youth Services Review, 76, 10–19.
McSherry, D. (2018). Remembering what the big friendly giants said: To understand outcomes, you first
need to understand context. Children Australia, 43(2), 91–94.
McSherry, D., Fargas Malet, M., & Weatherall, K. (2018). The Strengths and Difficulties Questionnaire
(SDQ): A proxy measure of parenting stress. The British Journal of Social Work, 49(1), 96–115.
McSherry, D., & Malet, M. F. (2017). Family foster care: Let’s not throw the baby out with the bathwater.
Children Australia, 42(3), 217–221.
McWey, L. M., Cui, M., & Pazdera, A. L. (2010). Changes in externalizing and internalizing problems of
adolescents in foster care. Journal of Marriage and Family, 72(5), 1128–1140.
Muris, P., Meesters, C., & van den Berg, F. (2003). The strengths and difficulties questionnaire (SDQ).
European Child & Adolescent Psychiatry, 12(1), 1–8.
Murray, L., Tarren-Sweeney, M., & France, K. (2011). Foster carer perceptions of support and training in
the context of high burden of care. Child & Family Social Work, 16(2), 149–158.
Newton, R. R., Litrownik, A. J., & Landsverk, J. A. (2000). Children and youth in foster care: Disentan-
gling the relationship between problem behaviors and number of placements. Child Abuse & Neglect,
24(10), 1363–1374. https ://doi.org/10.1016/S0145 -2134(00)00189 -7.
Nilsen, W. (2007). Fostering futures: A preventive intervention program for school-age children in foster
care. Clinical Child Psychology and Psychiatry, 12(1), 45–63.
Oosterman, M., Schuengel, C., Slot, N. W., Bullens, R. A. R., & Doreleijers, T. A. H. (2007). Disruptions in
foster care: A review and meta-analysis. Children and Youth Services Review, 29(1), 53–76.
Perkins, J. N. (2008). Foster parenting practices as predictors of foster child outcomes. University of Ottawa
(Canada).
Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal of School Psychology, 48(1), 85–112.
Roy, P., Rutter, M., & Pickles, A. (2000). Institutional care: Risk from family background or pattern of rear-
ing? The Journal of Child Psychology and Psychiatry and Allied Disciplines, 41(2), 139–149.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Meth-
ods, 7(2), 147.
Schoemaker, N. K., Wentholt, W. G. M., Goemans, A., Vermeer, H. J., Juffer, F., & Alink, L. R. A. (2019).
A meta-analytic review of parenting interventions in foster care and adoption. Development and Psy-
chopathology. https ://doi.org/10.1017/S0954 57941 90007 98.
Stott, T., & Gustavsson, N. (2010). Balancing permanency and stability for youth in foster care. Children
and Youth Services Review, 32(4), 619–625.
Shelton, K. K., Frick, P. J., & Wootton, J. (1996). Assessment of parenting practices in families of elemen-
tary school-age children. Journal of Clinical Child Psychology, 25(3), 317–329.
Shore, N., Sim, K. E., Le Prohn, N. S., & Keller, T. E. (2002). Foster parent and teacher assessments of
youth in kinship and non-kinship foster care placements: Are behaviors perceived differently across
settings? Children and Youth Services Review, 24(1–2), 109–134.
Singer, J. D., Willett, J. B., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change
and event occurrence. Oxford: Oxford University Press.
Stone, N. M., & Stone, S. F. (1983). The prediction of successful foster placement. Social Casework, 64(1),
11–17.
Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston, MA:
Pearson.
Tarren-Sweeney, M. (2008). Retrospective and concurrent predictors of the mental health of children in
care. Children and Youth Services Review, 30(1), 1–25.
Tarren-Sweeney, M. (2017). Rates of meaningful change in the mental health of children in long-term out-
of-home care: A seven-to nine-year prospective study. Child Abuse & Neglect, 72, 1–9.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
686
Child & Youth Care Forum (2020) 49:661–686
1 3
Tarren-Sweeney, M. T., & Goemans, A. (2019). A narrative review of stability and change in the mental
health of children who grow up in family-based out-of-home care. Developmental Child Welfare, 1(3),
273–294.
Team, R. C. (2018). R: A language and environment for statistical computing; 2015.
Tizard, B., & Hodges, J. (1978). The effect of early institutional rearing on the development of eight year
old children. Journal of Child Psychology and Psychiatry, 19(2), 99–118.
Van Andel, H. W. H., Post, W. J., Jansen, L. M. C., Kamphuis, J. S., Van der Gaag, R. J., Knorth, E. J., etal.
(2015). The developing relationship between recently placed foster infants and toddlers and their foster
carers: Do demographic factors, placement characteristics and biological stress markers matter? Chil-
dren and Youth Services Review, 58, 219–226.
Van Ginkel, J. R., Linting, M., Rippe, R. C., & van der Voort, A. (2019). Rebutting existing misconceptions
about multiple imputation as a method for handling missing data. Journal of Personality Assessment.
https ://doi.org/10.1080/00223 891.2018.15306 80.
Van Lier, P. A. C., & Crijnen, A. A. M. (1999). Alabama parenting questionnaire, Nederlandse vertaling
[Alabama Parenting Questionnaire, Dutch translation]. Unpublished Manuscript.
Van Oijen, S. (2010). Resultaat van pleegzorgplaatsingen: Een onderzoek naar breakdown en de ontwik-
keling van adolescente pleegkinderen bij langdurige pleegzorgplaatsingen. University of Groningen.
Van Widenfelt, B. M., Goedhart, A. W., Treffers, P. D. A., & Goodman, R. (2003). Dutch version of the
Strengths and Difficulties Questionnaire (SDQ). European Child & Adolescent Psychiatry, 12(6),
281–289.
Vanderfaeillie, J., Van Holen, F., Vanschoonlandt, F., Robberechts, M., & Stroobants, T. (2013). Children
placed in long-term family foster care: A longitudinal study into the development of problem behavior
and associated factors. Children and Youth Services Review, 35(4), 587–593. https ://doi.org/10.1016/J.
CHILD YOUTH .2012.12.012.
Winokur, M. A., Holtan, A., & Batchelder, K. E. (2018). Systematic review of kinship care effects on safety,
permanency, and well-being outcomes. Research on Social Work Practice, 28(1), 19–32.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center
GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers
and authorised users (“Users”), for small-scale personal, non-commercial use provided that all
copyright, trade and service marks and other proprietary notices are maintained. By accessing,
sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of
use (“Terms”). For these purposes, Springer Nature considers academic use (by researchers and
students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and
conditions, a relevant site licence or a personal subscription. These Terms will prevail over any
conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription (to
the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of
the Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may
also use these personal data internally within ResearchGate and Springer Nature and as agreed share
it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not otherwise
disclose your personal data outside the ResearchGate or the Springer Nature group of companies
unless we have your permission as detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial
use, it is important to note that Users may not:
use such content for the purpose of providing other users with access on a regular or large scale
basis or as a means to circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any
jurisdiction, or gives rise to civil liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association
unless explicitly agreed to by Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a
systematic database of Springer Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a
product or service that creates revenue, royalties, rent or income from our content or its inclusion as
part of a paid for service or for other commercial gain. Springer Nature journal content cannot be
used for inter-library loans and librarians may not upload Springer Nature journal content on a large
scale into their, or any other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not
obligated to publish any information or content on this website and may remove it or features or
functionality at our sole discretion, at any time with or without notice. Springer Nature may revoke
this licence to you at any time and remove access to any copies of the Springer Nature journal content
which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or
guarantees to Users, either express or implied with respect to the Springer nature journal content and
all parties disclaim and waive any implied warranties or warranties imposed by law, including
merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published
by Springer Nature that may be licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a
regular basis or in any other manner not expressly permitted by these Terms, please contact Springer
Nature at
onlineservice@springernature.com
Content uploaded by Anouk Goemans
Author content
All content in this area was uploaded by Anouk Goemans on May 06, 2020
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
