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Foster Parent Stress as Key Factor Relating to Foster Children’s Mental Health: A 1-Year Prospective Longitudinal Study

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Background Foster children are reported to often have mental health difficulties. To optimize foster children’s development chances, we need to know more about the characteristics 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 17 years 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 children’s mental health (internalizing, externalizing, and prosocial behaviors) using a three-wave longitudinal designResultsResults showed that levels of mental health were generally stable over time. Differences between foster children’s developmental outcomes were mainly predicted by foster parent stress.Conclusions Foster parent stress levels were high and consistently found to be the strongest 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.
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Child & Youth Care Forum (2020) 49:661–686
https://doi.org/10.1007/s10566-020-09547-4
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ORIGINAL PAPER
Foster Parent Stress asKey Factor Relating toFoster
Children’s Mental Health: A1‑Year Prospective Longitudinal
Study
AnoukGoemans1· RenateS.M.Buisman1· MitchvanGeel1· PaulVedder1
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 17years 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 ofEducation andChild Studies, Leiden University, Wassenaarseweg 52, 2333AKLeiden,
TheNetherlands
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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 etal. 2000; Tizard and Hodges 1978). Although foster care is
often considered the best alternative in case of out-of-home placement (Dozier etal. 2014;
Li etal. 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 etal. 2016a). However, there is large heterogeneity between
foster children with regard to their mental health outcomes (Goemans etal. 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 etal. 2019;
Oosterman etal. 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 etal. 2014; Newton et al. 2000;
Vanderfaeillie etal. 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 etal. 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 etal. 2007; Rubin etal.
2007). For example, the incidence of one or more previous placements indicates the poten-
tial risk of broken attachments and unsafe attachment representations (Newton etal. 2007;
Rubin etal. 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 etal.’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 etal. 2018; Winokur etal. 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
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consequence foster children’s mental health outcomes (Rubin etal. 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 etal. 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 etal. 1998; Lehmann etal. 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 etal. 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 etal. 2015).
Some studies found improved mental health outcomes for foster children over time (e.g.,
Ahmad etal. 2005; Barber and Delfabbro 2005; Fernandez 2009), while others did not rep-
licate these results (e.g., Leathers, Spielfogel etal. 2011; Perkins 2008) or even found that
foster children’s mental health deteriorated over time (e.g., Fanshel and Shinn 1978; Frank
1980; Lawrence etal. 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 etal. 2007). Moreover, longitudinal research on children in foster care can be
difficult in terms of recruitment, data collection, and follow-up (Jackson etal. 2012; Maas-
kant 2016), and is often characterized by high attrition rates and missing data (Goemans
etal. 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
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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 offoster children’s feelings of permanency and consequently
also their mental health outcomes (Rubin etal. 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 etal. 2008;
Maaskant etal. 2014; Winokur etal. 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 etal.
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 17years 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 58.98months (SD = 50.61,
range 0–214months), with 19.1% of the children being in their current placement for less
than 1year, 12.3% for 1 to 2years, 11.1% for 2 to 3years, 8.9% for 3 to 4years, 8.7% for 4
to 5years, 6.5% for 5 to 6years, 6.3% for 6 to 7years, 5.1% for 7 to 8years, 4.6% for 8 to
9years, 3.1% for 9 to 10years, 3.9% for 10 to 11years, and 10.4% for more than 11years
with a maximum of almost 18years (214months).
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.
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In October 2014 we started our longitudinal study in which we followed foster children
and their foster families for 12months. 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 3–17years, 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 17years 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 etal. 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 etal. (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 etal. 2008; Goodman etal. 2010; Van Widenfelt etal. 2003)
and in the Netherlands (Muris etal. 2003; Van Widenfelt etal. 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,
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0.83, and 0.85 for externalizing problems, and 0.75, 0.72, and 0.77 for prosocial behavior,
respectively.
Characteristics oftheFoster Child, theChild’s Care Experiences, andtheFoster 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 etal. 2008; Boyce, etal. 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 etal. 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 etal. 2003; Elgar etal.
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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 etal. 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 etal. 1992; Haskett etal. 2006), and the NOSI-K
has been previously used in studies on foster parents (Maaskant etal. 2016; Murray etal.
2011; Nilsen 2007; Van Andel etal. 2015). In the current study the internal consistency for
all three waves was 0.96.
DataAnalysis
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 etal. 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 etal. 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
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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 etal. 2011) revealed that
imputations did not converge (Grund etal. 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 Table1).
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 Table1. 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.
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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**
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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
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Prosocial Behavior
The results of the multilevel models on prosocial behavior are presented in Table2. 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 Table2.
Internalizing Behavior Problems
The results for the multilevel models on internalizing behaviors are displayed in Table3.
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
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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
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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
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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
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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
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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 Table3.
Externalizing Behavior Problems
The last set of multilevel models was run for externalizing behaviors. The results are pre-
sented in Table4. 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, Model7, included the significant predictors (age, foster parent stress) and
is presented in Table4. 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 etal. 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 etal. (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
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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
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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
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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 etal. 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 etal. 2011; McSherry etal. 2018; Murray etal.
2011). However, only a few longitudinal studies have examined the directionality of the
effects (Gabler etal. 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 etal. 2018a; Lohaus etal. 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 etal. (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
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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 etal. 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 etal. 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 etal. 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 etal. 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 etal. 2000; Dubowitz etal. 1993; Heflinger etal. 2000; Maas-
kant etal. 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 etal. (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 etal. 2019; Oosterman etal. 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 etal. 2000; Rubin etal. 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.
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Limitations andDirections forFuture 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
etal. 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 etal. 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 etal. 2012). Strategies for longitudinal research with foster
children as described by Jackson etal. (2012) are helpful to prevent attrition in longitudinal
designs. Example strategies mentioned by Jackson etal. (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
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Child & Youth Care Forum (2020) 49:661–686
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(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 etal. 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 etal. 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 etal. (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 etal. 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
Conict 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/.
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... Associations between mental health and placement stability is a likely cyclical relationship, with young people with greater behaviour or emotional difficulties potentially more difficult for carers to manage (e.g. increasing carer stress, which is then related to poorer child mental health; Goemans et al., 2020), but the breakdown of placements also leading to further entrenchment of the young person's difficulties (e.g. Gilbertson & Barber, 2003;Rock et al., 2013;Sinclair & Wilson, 2003). ...
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While we know there are high rates of mental health difficulties amongst young people in care (i.e., social welfare-involved children), there is limited evidence on the longitudinal development of these problems, particularly from when they enter the care system. Using the routinely collected carer-reported strengths and difficulties questionnaire (SDQ), we explored internalising (emotional and peer) and externalising (conduct and hyperactivity) difficulties for 672 young people across their first three years in the UK care system (2-16yrs, 51% boys, 76% Caucasian). In all cases stable profiles (resilient or chronic) were most common, while changing profiles (recovery or delayed) were less common. Findings show that entry into the care system is not enough of an intervention to expect natural recovery from mental health difficulties. Number of placements and being separated from siblings were associated with greater difficulties. Implications for child welfare and mental health systems are discussed.
... Aufgrund der Akkumulation von Risikofaktoren liegt die Prävalenz psychischer Störungen bei fremdplatzierten Kindern und Jugendlichen mit 49 % bis 76 % (Bronsard et al., 2016;Bronsard et al., 2011;Goemans, Buisman, van Geel & Vedder, 2020;Jozefiak et al., 2016) deutlich höher im Vergleich zur Prävalenz von 10 % bis 20 % in der Allgemeinbevölkerung (Merikangas et al., 2010;Polanczyk, Salum, Sugaya, Caye & Rohde, 2015). Zudem gibt es eine deutlich erhöhte Prävalenz komorbider Störungen bei fremdplatzierten Kindern und Jugendlichen, was die Prognosen dieser Kinder und Jugendlichen zusätzlich beinträchtigen kann (Jozefiak et al., 2016). ...
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... This specific theoretical framework states that family dynamics are not to be considered unidirectional, but rather bi-or, better yet, multidirectional and on several levels: Characteristics of individual members shape their relationships with others, but dyadic interactions within the family also have an impact on one another and are, in turn, influenced by family and contextual factors. These mutual influences, unfolding over time, continuously determine the specific family setup and functioning and thereby affect the development of children as well [50][51][52]. Consequently, a deeper understanding of the interplay between foster parents and children variables is relevant, as it could provide a more detailed perspective of the foster-care experience. ...
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Background: Mental health during a person’s adolescence plays a key role in setting the stage for their mental health over the rest of their life. Hence, initiatives that promote adolescents’ wellbeing are an important public health goal. Helping others can take a variety of forms, and the literature suggests that helping others can positively impact a person’s wellbeing. However, there is a lack of data that synthesizes the impact of helping others on adolescents’ wellbeing. Therefore, this review aims to synthesize the available evidence related to helping others and to youth wellbeing. Methods: A scoping review search was undertaken with no date restrictions. CINAHL, Medline and PyschINFO, were searched for studies that analyzed the relationship between helping others and youth mental health. Results: Data from 213 papers were included in the scoping review. Three main themes were observed: (1) the relationship between helping others and mental health outcomes among youths (positive and negative); (2) factors associated with youth engagement in prosocial behavior (facilitators and barriers); (3) the impact of interventions related to helping others, and to youth mental health (positive and negative). Conclusions: An overwhelmingly positive relationship exists between youth prosocial behavior and its influence on youth mental health.
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The present review sought to address the following questions: What evidence is there that long-term, family-based out-of-home care (OOHC) has a general, population-wide effect on children’s mental health such that it is generally reparative or generally harmful? Does entry into long-term OOHC affect children’s mental health, as evidenced by prospective changes over the first years in care? And, is the reparative potential of long-term, family-based OOHC moderated by children’s age at entry into care? Fourteen studies were identified for review. We found no consistent evidence that family-based OOHC exerts a general, population-wide effect on the mental health of children in care; or that entry into care has an initial effect on children’s mental health; or that children’s age at entry into care moderates their subsequent mental health trajectories. Instead, several longitudinal studies have found that sizable proportions of children in care manifest meaningful improvement in their mental health over both short- and long-term time frames and that similarly sizable proportions experience meaningful deterioration in their mental health. Rather than asking whether long-term, family-based care is generally reparative or harmful for the development of previously maltreated children, future investigations should instead focus on identifying the systemic and interpersonal characteristics of care that promote and sustain children’s psychological development throughout childhood—and those characteristics that are developmentally harmful (i.e., for which children is the experience of care beneficial, and for which children is it not?). The review concludes with recommendations for the design of improved cohort studies that can address these questions.
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Foster and adoptive parents often face challenges while taking care of children who, due to their adverse early life experiences, are at risk of developing insecure attachment relationships, behavior problems, and stress dysregulation. Several intervention programs have been developed to help foster and adoptive parents to overcome these challenges. In the current study, a series of eight meta-analyses were performed to examine the effectiveness of these intervention programs on four parent outcomes (sensitive parenting, k = 11, N = 684; dysfunctional discipline, k = 4, N = 239; parenting knowledge and attitudes, k = 7, N = 535; parenting stress, k = 18, N = 1,306), three child outcomes (attachment security, k = 6, N = 395; behavior problems, k = 33, N = 2,661; diurnal cortisol levels, k = 3, N = 261), and placement disruption ( k = 7, N = 1,100). Results show positive effects for the four parent outcomes and child behavior problems, but not for attachment security, child diurnal cortisol levels, or placement disruption. Indirect effects on child outcomes may be delayed, and therefore long-term follow-up studies are needed to examine the effects of parenting interventions on children.
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Missing data is a problem that occurs frequently in many scientific areas. The most sophisticated method for dealing with this problem is multiple imputation. Contrary to other methods, like listwise deletion, this method does not throw away information, and partly repairs the problem of systematic dropout. Although from a theoretical point of view multiple imputation is considered to be the optimal method, many applied researchers are reluctant to use it because of persistent misconceptions about this method. Instead of providing an(other) overview of missing data methods, or extensively explaining how multiple imputation works, this article aims specifically at rebutting these misconceptions, and provides applied researchers with practical arguments supporting them in the use of multiple imputation.
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Background Foster parents experience multiple challenges; however, managing problematic child behaviors can be especially difficult. Given the ecological nature of challenges associated with fostering, it is imperative that researchers identify means for combatting foster parent stress and factors that may contribute to placement disruption. Objective The purpose of this paper is to examine the importance of social support for foster parents, in regards to confidence and satisfaction, as well as perceived challenges with fostering. Additionally, social support was evaluated as a moderator between reported child behaviors and foster parents’ confidence and satisfaction with fostering. Method This study included 155 licensed foster caregivers across the United States. Participants completed standardized measures (i.e., child behaviors, satisfaction and challenges as a foster parent, social support) through an online survey after being recruited via social media. Variables were analyzed through simple and hierarchal linear regressions. Results Findings indicate that social support significantly predicted confidence and satisfaction as a foster parent; intensity of child behaviors is negatively associated with confidence as a foster parent and positively associated with an overall perception of challenges related to fostering; and social support moderates the relationship between perceived problem with child behaviors and perceived challenging aspects of fostering. Conclusion This study indicates social support may be a protective factor for foster parents in regards to child behaviors and challenges associated with fostering. Social support is linked to variables that directly influence placement stability.
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“For me context is the key – from that comes the understanding of everything.” Kenneth Noland, American painter, April 10, 1924–January 5, 2010
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The significant mental health needs of young people in out-of-home care has been well-documented. However, there is little empirical evidence on the timing or development of these difficulties, once these young people have been removed from the maltreatment-environment. Such information may provide useful clinical insight in to how problems develop and persist and whether intervention timings may allow for the prevention of later mental health problems. The current service-data study explored the emotional and behavioural symptom trajectories of 207 young people under the long-term care of a local authority in the South West of England, over their first five years in the care system. Data were extracted from the yearly carer-completed strengths and difficulties questionnaire - providing an index of emotional problems, peer problems, conduct problems and hyperactivity. Trajectories were analysed using growth mixture modelling. For most domains the largest trajectories were chronic symptom profiles, where young people were rated in the abnormal range from their first year in care and remained in this range across the full five years. These young people had significantly more placement moves than their peers on resilient trajectories. There was some evidence that later age of removal was associated with more chronic internalising problems. Overall, findings demonstrate the significant mental health needs of young people in care and particularly highlight that, in many cases, the removal from the adverse environment is simply not enough to expect a young person in care to be resilient to their earlier experiences.
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The Strengths and Difficulties Questionnaire (SDQ) (Goodman, 1997) is a brief behav-ioural screening questionnaire for three-to sixteen-year-olds. It is commonly used in clinical practice and research, particularly in the UK, and is completed by parents, carers and teachers. The measure was utilised in a cross-sectional phase of a longitudinal study of children in care, namely the Care Pathways and Outcomes Study, alongside a measure of parenting stress, the Parenting Stress Index-Short Form (PSI-SF) (Abidin, 1995), with a sub-sample of children (n ¼ 72) aged nine to fourteen and their parents and carers. A Pearson Correlation Coefficient indicated a strong positive correlation between these two measures (r¼ 0.71), with normal and abnormal scores on one measure corresponding to normal and abnormal scores on the other. Consequently, it is argued that the SDQ may be considered a proxy measure of parenting stress, with scores in the clinical range being highly predictive of clinical levels of parenting stress. As such, SDQ-informed interventions for adopted children and children in care and others where behavioural problems have been detected should be developed to include a consideration of the needs of parents and carers, specifically in relation to reducing levels of parenting stress.
Article
Background: The majority of children in foster care 24 months or longer experience three or more placements. Children's behavior problems are a primary contributor to multiple moves, but little is known about how behavior problems and other stressors lead to disruptions. This study focused on foster parents' experiences of parenting a child at risk for moves using the determinants of parenting model (Belsky, 1984) to identify potential correlates of difficult parenting experiences and placement disruption. Objective: To identify factors associated with difficult parenting experiences and placement disruption. Participants: Foster parents (N = 139) caring for children age 8-14 in long term foster care with a history of two or more moves were randomly selected in a large Midwestern state in the U.S. Methods: Participants completed a 90-minute telephone interview (86% response rate). Placement moves were tracked prospectively for two years. Parenting experiences and disruption were analyzed using multiple and logistic regression. Results: Results support aspects of the determinants of parenting model. Behavior problems, children's risk to others, low support, and stress were significantly associated with more difficult parenting experiences (βs = .28, .22, .18, .19, respectively, ps < .05), and more difficult parenting experiences strongly predicted placement disruption (p < .01). Risk to others also predicted disruption before including parenting experiences, with this association becoming nonsignificant after including parenting experiences. Unexpectedly, African American foster parents had a higher risk for disruption, despite more positive parenting experiences. Conclusions: These findings support attending to foster parents' parenting experiences, children's risk to others, social support and stress to better support placements of children at risk for disruption.
Article
Foster care is the preferred type of out-of-home placement for children and youth when they are not able to live with their own parents. However, placement instability, and its effect on children's behavioral well-being, remains a major issue in foster care. Ten multilevel meta-analyses were performed to examine factors that can affect instability of foster care placement. We included 42 studies (published between 1990 and 2017) examining putative factors associated with placement instability, which yielded 293 effect sizes. Indications of publication bias were found, but the trim and fill procedure confirmed the main findings. Medium significant effects were found for child behavioral problems (r=0.35), (non-)kinship care (r=0.31), and quality parenting (r=0.29). Smaller effects were found for age of the child (r=0.25), placement with(out) siblings (r=0.16), and history of maltreatment of the child before placement (r=0.14). The effects were generally modest, but showed generalizability across continents and time. The findings can be used to improve interventions for the prevention of placement instability in foster care, and further investigations.
Article
This paper focuses on the longitudinal relationships between foster children’s mental health problems and parental stress across a 1-year interval with three measurements. A sample of 94 foster children and a comparison group of 157 biological children and their families participated in this study. The age of the children was between 2 and 7 years. At the initial assessment, the foster children had been in their foster families since 2–24 months. Based on Child Behavior Checklist (CBCL) scores, the results indicated increased internalizing and externalizing mental health problems in the foster children group. Both mental health scores remained rather stable across the longitudinal assessments in foster as well as in biological children. Internalizing as well as externalizing scores were substantially correlated with parental stress in both samples. Moreover, changes in mental health scores were associated with changes in parental stress. However, cross-lagged panel analyses showed no clear pattern of temporal relationships between children’s mental health scores and parental stress. Implications as well as strengths and limitations of the current study are addressed in the Discussion section.