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J Youth Adolescence
DOI 10.1007/s10964-017-0721-5
EMPIRICAL RESEARCH
Appreciating Complexity in Adolescent Self-Harm Risk Factors:
Psychological Profiling in a Longitudinal Community Sample
Sarah Stanford
1
●Michael P. Jones
1
●Jennifer L. Hudson
1
Received: 16 March 2017 / Accepted: 11 July 2017
© Springer Science+Business Media, LLC 2017
Abstract Past research identifies a number of risk factors
for adolescent self-harm, but often fails to account for
overlap between these factors. This study investigated the
underlying, broader concepts by identifying different psy-
chological profiles among adolescents. We then compared
new self-harm rates over a six-month period across different
psychological profiles. Australian high school students (n=
326, 68.1% female) completed a questionnaire including a
broad range of psychological and socioenvironmental risk
and protective factors. Non-hierarchical cluster analysis
produced six groups with different psychological profiles at
baseline and rate of new self-harm at follow-up. The lowest
rate was 1.4% in a group that appeared psychologically
healthy; the highest rate was 37.5% in a group that dis-
played numerous psychological difficulties. Four groups
with average self-harm had varied psychological profiles
including low impulsivity, anxiety, impulsivity, and poor
use of positive coping strategies. Identifying multiple pro-
files with distinct psychological characteristics can improve
detection, guide prevention, and tailor treatment.
Keywords Self-harm ●Risk factors ●Adolescence ●
Psychological profiles
Introduction
Self-harm is common among teens, with community pre-
valence estimated at 5–15% and even higher (Brunner et al.
2014; Madge et al. 2008; Moran et al. 2012; Stallard et al.
2013). Self-harm rates are thought to peak in mid-adoles-
cence, with an average onset of self-harm around age 12–14
(Jacobson and Gould 2007). Rates gradually decrease
throughout older adolescence and the emerging adult years
(Moran et al. 2012). Adolescent self-harm is a considerable
source of stress for those supporting a teen through self-
harm, including family and friends (McVey-Noble et al.
2006), and those who work with teens in schools and in
other community settings (Best 2006). Two reasons for
stress are: concern for the teen’s physical safety, and the
potential for contagion among peers. First, while self-harm
often occurs without suicidal intent, self-harm is a strong
risk factor for suicide attempt (Taliaferro and Muehlenkamp
2014) and completed suicide (Yoshimasu et al. 2008).
Second, there is evidence that self-harm by friends is
associated with increased risk of self-harm (O’Connor et al.
2009), leading to concern that social contagion may occur
following self-harm. In light of these concerns, research is
required in order to better understand, prevent, and treat
self-harm (Robinson et al. 2016). This study contributes to
this knowledge gap by developing our understanding of
psychological risk factors for self-harm in adolescence
using a profile analysis.
This study adopts a broad definition of the term ‘self-
harm”as any behavior that is intentionally self-inflicted with
immediate physical consequences (Morgan 1979), includ-
ing self-harm with and without suicidal intent. It is difficult
to draw distinct categories between suicidal and non-
suicidal self-harm since suicidal intent is complex and can
be ambiguous and transient (Brunner et al. 2014; Hawton
*Sarah Stanford
Sarah.stanford@mq.edu.au
1
Macquarie University, Balaclava Rd, North Ryde, NSW 2109,
Australia
et al. 2010; Kapur et al. 2013; Lofthouse and Yager-
Schweller 2009). Indeed, Joiner’s interpersonal theory of
suicide (2005) proposes that self-harm desensitizes people
towards self-destructive behavior, which may increase the
likelihood of people acting on suicidal thoughts.
There is a growing understanding of the factors asso-
ciated with self-harm, although much is yet to be under-
stood regarding the mechanisms and interactions at play
(Hawton et al. 2012). In selecting factors to focus on in this
study, we prioritized factors that were included in large
community adolescent samples (for more detail see Stan-
ford and Jones 2015). Notable large international studies
include the Saving and Empowering Young Lives in Eur-
ope (SEYLE) project with 12,068 adolescents (Brunner
et al. 2014) and the Child & Adolescent Self-harm in
Europe (CASE) study consisting of 30,477 adolescents
(Madge et al. 2011). These larger studies sit within a
growing literature base that includes a number of smaller
but nonetheless substantial studies. For example, Heerde
and colleagues (2015) report on a longitudinal study of
3876 adolescents in Australia and the US participating in
the International Youth Development Study; Mars and
colleagues (2014) present findings from 4799 adolescents in
the UK participating in the Avon Longitudinal Study of
Parents and Children. Findings from this research body
identify psychological risk factors that are consistently
associated with increased self-harm; from these we selected
depressive and anxiety symptoms, self-esteem, impulsivity,
and attention and conduct difficulties. Prior work has
focused more on risk factors rather than protective factors
(Fortune and Hawton 2005) but protective factors are an
important area for future research (Fliege et al. 2009).
Psychological protective factors include coping strategies
(Guerreiro et al. 2015), meaning in life (Kleiman and
Beaver 2016) and life satisfaction (Heisel and Flett 2004).
While the focus is on psychological factors, this study
includes a number of social and environmental factors that
are frequently included in risk factor research. Factors
commonly measured in association with self-harm include
age, gender, ethnicity, parental divorce/separation, bullying,
and self-harm modeling (Brunner et al. 2014; Hawton et al.
2012). Protective factors include supportive relationships
and spirituality (Brunner et al. 2014).
There are several limitations that are commonly
acknowledged in past self-harm risk factor research. For
example, using clinical samples limits generalizability to
community settings, cross-sectional designs limit our
understanding of the causal pathway, and using a narrow set
of factors limits comparability between variables and fails to
account for the effect of unmeasured variables (Wilcox et al.
2012). However, there is an important conceptual limitation
that is less often discussed. Research typically approaches
risk factors as distinct components. That is, depression,
anxiety, and self-esteem, for example, are considered
unique factors. Yet we know that there is considerable
overlap between these factors. A potential problem with
assigning risk factor status to a specific construct is that it
might just be a proxy for the “real”risk factor. Therefore
researchers are beginning to develop another way of
approaching the risk factor problem to consider multiple
overlapping variables simultaneously.
In recent years, a small contingent of research has begun
to take a profile approach to investigate self-harm in more
complex ways. Somer and colleagues (2015) explain that
profile or latent class analyses identify comparatively
homogeneous subpopulations from within the hetero-
geneous population of people who report self-harm. An
improved understanding of these groups could assist in
understanding people who self-harm, developing interven-
tions, informing treatment decisions, and developing mod-
els to explain self-harm (Klonsky and Olino 2008; Somer
et al. 2015). However, past profile research has primarily
focused on the characteristics of self-harm behavior rather
than on the psychological profile of those who self-harm.
For example, research has identified subtypes within those
who report self-harm based on severity and method of self-
harm in adolescents (Somer et al. 2015) and in adults
(Hamza and Willoughby 2013; Bracken-Minor et al. 2012;
Klonsky and Olino 2008; Whitlock et al. 2008). A con-
sistent finding across these studies is that increased self-
harm severity and frequency was associated with increased
psychological pathology and more severe suicidal behavior.
Research using adolescent samples has also focused on
combined psychological, suicidal and sociodemographic
factors (Jiang et al. 2010), or a range of risk taking beha-
viors including self-harm (Thullen et al. 2015). In each of
these studies, aspects of self-harm and/or suicidal behavior
were included in the variables used to create the profiles
along with other risk factors. Researchers have used the
profile approach to identify variability in relationships with
parents and peers in adolescent (Lundh et al. 2009) and
university samples (Martin et al. 2016).
In contrast to prior work, this study focuses on psycho-
logical risk factors for self-harm. It will assign individuals
to groups based entirely on psychological profiles and
explore how these profiles relate to self-harm behavior at
follow-up. Since this is a study of risk factors, self-harm
behavior is not included in the profile creation. Instead, the
analysis focuses on the variables thought to be earlier in the
causal pathway (Kraemer et al. 2001). Research focused on
psychological profiles is extremely limited. In our previous
Australian community sample, adolescents grouped natu-
rally into six distinct profiles of individuals based on a range
of factors including depression, anxiety, low self-esteem,
coping strategies, and impulsivity (Stanford and Jones
2012). Two profiles were characterized by having an
J Youth Adolescence
undesirable psychological profile that could be loosely
described as psychopathology. The six profiles of indivi-
duals could be divided into three with comparatively low
rates of self-harm (5–16% lifetime prevalence) and three
with comparatively high rates (25–58% lifetime pre-
valence). Not surprisingly, the three groups that could be
broadly described as having a “normal”psychological pro-
file had low self-harm rates. Of the three high self-harm rate
profiles, one was characterized only by high scores on
impulsivity but was otherwise unremarkable (lifetime self-
harm prevalence 33%). The two remaining high self-harm
rate profiles were both characterised by psychological
pathology, but distinguishable by their use of coping stra-
tegies. One pathological group demonstrated positive cop-
ing strategies, and lifetime self-harm prevalence was 25%.
The other group with psychological pathology had poor
coping and low social support; lifetime self-harm in this
group was 58%. However its cross-sectional design and
combining high school and university students in the sam-
ple limited this study.
Current Study
The current study reports on 326 Australian high school
students who completed a baseline survey and a six-month
follow-up. Since the mechanisms and interactions under-
lying self-harm are not yet well understood (Hawton et al.
2012), this study aims to deepen the current understanding
of the psychological risk factors for self-harm. The aim is to
compare the rate of new self-harm at six-month follow-up
in groups with different psychological profiles. We hypo-
thesize that profiles with poorer psychological function at
baseline will be associated with higher rate of new self-
harm at six-month follow-up, as found in past cross-
sectional research (Somer et al. 2015; Stanford and
Jones 2012). To further understand these groups, we
will describe a range of social and environmental
factors. The study extends prior work by using a long-
itudinal design in an adolescent sample. These results will
assist teachers, counselors, and others who work with
adolescents in community settings to identify adolescents
who may be at risk of future self-harm. Applications of
these findings are pertinent for both prevention and inter-
vention strategies.
Methods
Participants
Data were collected as part of the Youth Coping Project to
investigate youth coping and welfare. This article reports on
the subset who completed the baseline survey and a six-
month follow-up (n=326), which is part of a larger base-
line sample (n=1521). Participants were in year 7–11 at
baseline in 2014, and year 8–12 at follow-up 6 months later
in 2015. The sample was 68.1% female (n=222) and mean
age was 14.1 (SD =1.4). These students were drawn from
four mainstream co-educational schools and one girls
school. The majority of students were born in Australia
(90.8%) and their biological parents were married (81.3%).
All participating schools were private, fee-charging schools
(Independent or Catholic), however the financial profile of
the participating schools varied. Median weekly income
(based on Census 2011) for the suburbs of the schools
ranged from $711 to $2513 and annual school fees for a
Year 7 student ranged from $5000 to $13,655. There was a
small degree of variability in mental health and socio-
demographic factors between the schools, as expected given
the geographical area covered. Inclusion required a satis-
factory level of competence in reading and comprehending
English. Participants and their parents provided informed
consent. The study had ethical approval from Macquarie
University. Participation rate varied by school, depending
the school’s success in collecting parental consent and
availability for students to participate during class time.
Participating students received a small token of appreciation
(i.e., chocolate or novelty gift) and participating schools
received a welfare report summarising data for their school.
The overall response rate for the first survey (Survey 1) was
30.2%; of these, 58.7% completed the follow-up survey
(Survey 2).
Measures
Students completed the online questionnaire during class
time and most students completed the survey in 15–25
minutes. Measures were selected to prioritize factors with
strong prior association with self-harm and to include a
number of protective factors. We selected brief, validated
scales where possible. Scales were not diagnostic.
Self-harm
Self-harm behavior was assessed in two parts. The first
question asked broadly about lifetime self-harm: “Have you
tried to hurt yourself? You should answer “Yes”if you have
TRIED to hurt yourself, whether or not you were success-
ful. You should NOT include hurting yourself by accident”
(response options No/Yes). The second part asked more
specifically about six-month self-harm frequency, with
response options “None, I have not self-harmed in the last
6 months; 1; 2–5; 6–10; 11+“(Lloyd-Richardson et al.
2007). This approach is similar to brief measures of self-
harm used in prior research (Haavisto et al. 2005; Hay and
J Youth Adolescence
Meldrum 2010; Kaminski et al. 2010; Tolmunen et al.
2008) and past research indicates that adolescents are able
to accurately self-code behavior (Stanford and Jones 2010).
Self-harm frequency was dichotomized into Occasional (≤5
occurrences) and Repetitive self-harm (6+occurrences).
Self-harm modeling was measured by asking how many
friends and how many family members have hurt them-
selves on purpose in the last six months.
Depression and anxiety
Depressive and anxiety symptoms were measured using the
14-item Hospital and Anxiety Depression Scale (HADS),
originally developed by Zigmond and Snaith (1983). Par-
ticipants responded on a four-point likert scale from “Most
of the time”to “Not at all”to items such as “I feel tense or
wound up”(anxiety symptoms) and “I still enjoy the things I
used to enjoy”(depressive symptoms). The HADS has been
used in previous adolescent self-harm research (e.g., Madge
et al. 2008) and has been shown to have adequate test-retest
reliability and good discriminant validity in adolescent
samples (White et al. 1999). Internal consistency in our
sample was good for depressive and anxiety symptoms
(Cronbach’s alpha .72 and .83, respectively).
Self-esteem
Self-esteem was measured with the ten-item Rosenberg
Self-Esteem Scale (RSES). It measures self-acceptance,
self-respect, and positive self-evaluation on a 4-point scale
from “strongly agree”to “strongly disagree”. The RSES has
shown strong internal consistency, test-retest reliability, and
convergent validity (Swenson 2003), and high self-esteem
was negatively correlated with emotional and behavioral
disorders for most age/gender combinations (r=−.42 to
−.65) (Bagley and Mallick 2001). Internal consistency in
our sample was good (Cronbach’s alpha .90).
Conduct and attention difficulties
Difficulties with conduct and attention were measured using
the Externalizing (conduct) and Attention subscales of the
17-item version of the Pediatric Symptom Checklist (PSC).
The youth-report PSC-17 has been used previously in
adolescent samples (Duke et al. 2005; Roffman et al. 2001).
Higher total score correlated negatively with higher self-
esteem (r=−.37, p<0.001) and with getting into trouble
(r=−.37) (Roffman et al. 2001). In our sample, Cronbach’s
alphas were adequate (attention subscale: .78; externalising
(conduct) subscale: .70).
Impulsivity
Impulsivity was measured using six items from Plutchick’s
Impulsivity scale, as used in prior self-harm research (e.g.,
Madge et al. 2008). An example item is “I plan ahead,”with
four likert response options from “Almost never”to “Very
often.”As expected, impulsivity correlated with attention
difficulties (r=.42, p<.001) and conduct difficulties
(r =.35, p<.001). Internal consistency was lower than
ideal in our sample (Cronbach’s alpha: .58).
Coping strategies
Coping was measured using a 14 item shortened version of
the Ways of Coping Questionnaire adapted by Piko (2001);
see also Folkman et al. 1986. Students were asked to “think
about a difficult or negative experience you have been
through in the last year. How much did you use these ways
of coping?”with five response options (“None”to “Very
much”). An example item is “I made a plan of action and
followed it.”Exploratory factor analysis in two-thirds of the
sample produced three factors according to Kaiser’s criter-
ion, which appeared to be positive coping, negative coping,
and wishful thinking. For simplicity, we trialed a two-factor
solution and wishful thinking sat well with the negative
factors, offering a comparable fit to the three-factor solution
(see Table 3). Each one of our factors aligned with two of
Piko’s factors. For example, Piko’s support-seeking and
problem-analyzing factors were represented by positive
coping. There were two exceptions. “Tried to look on the
bright side”sat with positive coping in our sample, whereas
in Piko’s sample this item was on the negative coping sub-
scale. Prayer fit with the negative coping strategies in Piko’s
sample, but sat with the positive strategies in our sample, in
which 70% identified as Christian. This may reflect cultural
differences in optimism in the Australian culture and the
Christian faith in the participating schools. Cronbach’s alpha
was adequate for positive (.78) and negative (.69) scales.
Confirmatory factor analysis in the remaining one-third of
the sample broadly supported the two-factor solution. The fit
measures were lower than ideal, although broadly supportive
of the two-factor solution. The likelihood ratio test suggests
that the original and confirmatory models are different (χ
2
=
298.7, df =71, p<.001). The root mean square error of
approximation and comparative fit index were slightly
higher than ideal (.78 and .87, respectively). Further research
is needed to explore the validity of this measure and
applicability across different cultures and subcultures.
Meaning in life
Meaning in life was measured using the three-item (short-
form) Meaning in Life scale, with a five-point scale for
J Youth Adolescence
responses (“Not at all true”to “Completely true”; Kobau
et al. 2010). Participants were asked to take a moment to
think about what makes your life feel important to them. An
example item is “My life has a clear sense of purpose.”
Kobau reports acceptable internal consistency and relia-
bility (α=.89) and correlations with autonomy, compe-
tency, and relatedness show reasonable convergent validity
(r≥.63). In our sample Cronbach’s alpha was high at .91.
Life satisfaction
The five-item Satisfaction With Life Scale (SWLS) mea-
sures global life satisfaction with good internal consistency,
test-retest reliability, and correlations with other measures
of subjective wellbeing and personality characteristics
(Diener et al. 1985). The SWLS has been used in adolescent
samples (Neto 1993). In our sample Cronbach’s alpha was
high at .89.
Sociodemographic variables
Participants reported age, gender, country of birth, parent’s
marital status and number of older and younger siblings.
Supportive relationships
The Vaux Social Support Record measured connectedness
to family, peers, and adults at school (Vaux 1988). Three
items for each domain measure practical and emotional
support, rated on a three-point scale of “Not at all”,to“A
lot”. This version has been used in previous self-harm
research with Cronbach’s alpha indicating good internal
consistency (.85 for adults at school; .91 for family mem-
bers; .90 for peers; Kaminski et al. 2010), which was similar
to our sample (friends.82; family .80; school .82).
Bullying
Being a victim of bullying and bullying others were mea-
sured through selected items from Rigby’s Bullying Pre-
valence Questionnaire (Rigby and Slee 1993). They report
Cronbach’s alpha showing adequate internal reliability for
the victim (.75–.78) and bully (.78–.86) scales, and low
correlation between the two scales (r<.20). Our sample
showed similar patterns for Cronbach’s alpha (victim .84;
bully .67) and correlation between scales (r =.27).
Religious beliefs and practices
Students were instructed to mark “strongly disagree”or “not
at all”if the statements were not relevant, and to substitute
words that fit with your religious beliefs and practices.
Importance of faith was measured using the five-item short
version of the Santa Clara Strength of Religious Faith
Questionnaire (SCSRFQ) (Plante et al. 2002). This measure
is designed for use with multiple religious traditions and has
demonstrated good reliability and validity in a range of
settings (Plante et al. 2002). Religious coping was measured
using a shortened, adapted version of the brief measure of
religious coping (Brief RCOPE), which measures positive
and negative patterns of religious coping methods (Parga-
ment et al. 1998). Positive patterns include religious for-
giveness and seeking spiritual support; negative patterns
surveys spiritual discontent and viewing God as punishing.
The scale was shortened from 14 items to eight by taking
the top four items on each scale (positive and negative); two
items were similar in the top four for negative coping, so
one was excluded and the next highest loading item was
chosen. The items were reworded to adapt to adolescents
e.g., changed “Sought help from God in letting go of my
anger”to “Asked God to help me let go of my anger”.
Responses on likert scale “Not at all”to “A lot”. Cronbach’s
alphas were .93 for the positive scale and .90 for the
negative scale. As expected, the two scales showed minimal
correlation (r =.13). Positive religious coping was corre-
lated with Strength of Faith (r=.41, p<.001) but negative
religious coping was not (r=−.13, p<.001).
Procedure
We developed this project in collaboration with schools,
with ethical approval from Macquarie University. Pre-testing
from adolescents and adults provided positive feedback. We
presented the survey as the Youth Coping Research Project,
and invited students to participate to help us understand what
life is like for young people and how they cope with chal-
lenges. The survey was broad and the self-harm measure-
ment was brief, therefore it was not considered advantageous
to draw attention to self-harm beyond listing it in the study
description. The project had a dual-purpose in that partici-
pating schools received a welfare feedback report that
overviewed mental health and wellbeing.
Students and parents provided informed consent after
receiving the information and consent forms through printed
and electronic communication. The information described
the aim and procedures, and included a list of domains
included in the survey. We reminded the school community
about the survey using all forms of school communication
available, including assembly announcements, roll call
reminders, and paper and email newsletters. This commu-
nication emphasized that the survey was both voluntary and
anonymous. Students completed the questionnaire online to
reduce the risk of socially desirable responses and to enable
efficient data collection (Booth-Kewley et al. 2007; LaBrie
et al. 2006). After completing the survey, students were
informed of support available within and outside the school
J Youth Adolescence
(verbally and through printed materials), and researchers
were available to discuss any questions that arose. Students
filled in a support request form, and members of the
school’s welfare team followed students who responded
positively. To enable data matching with the second survey,
students created an ID code. This included the last two
letters of their first name, last two letters of their last name,
first letter of their first name, their date of birth, and number
of older siblings.
Analytic Approach
Step 1: we sought to create parsimonious measures of
psychological traits by creating composite “components”
that combine multiple individual variables. This helps to
avoid any single construct from dominating the next step of
forming profiles. This was achieved using principal com-
ponents analysis followed by orthogonal (varimax) rotation
with the following variables: depressive and anxiety
symptoms, self-esteem, attention and conduct difficulties,
impulsivity, positive and negative coping, meaning in life,
and life satisfaction. Step 2: we used a non-hierarchical
cluster analysis to allocate students to mutually exclusive
groups (profiles) based on the latent components created in
Step 1. The aim was to form independent groups that are
internally homogenous but different from the other profiles.
The non-hierarchical approach does not pre-determine how
many profiles to form. Therefore we considered a number of
profile solutions from one to ten profiles and identified the
point of inflection where the within-profile homogeneity
started to plateau using the Euclidean distance (Fig. 1). The
profile of psychological variables was interpreted to char-
acterize the distinguishing features of each profile. Step 3:
we compared rates of new self-harm at six-month follow-up
across profiles, which represent distinct psychological pro-
files. We compared the percentage within each profile who
report new six-month self-harm among students who did
not report recent self-harm at baseline. Step 4: we described
the psychological profile and self-harm rates at baseline for
each profile, followed by other social and environmental
factors. Given the non-normal distribution present in many
psychological and demographic variables, we compared
traits across profiles using the Pearson Chi-Square tests for
categorical variables and Kruskal-Wallis tests for numeric
variables. Pairwise comparisons between groups similarly
used Pearson Chi-Square tests and Mann-Whitney tests.
Results
Six-month self-harm prevalence at baseline was 12.3%
occasional (n=40) and 5.2% repetitive (n=17). In Step 1
described above, we found three components that
represented the individual psychological variables (Table
1). “Poor coping”included below average use of positive
coping strategies, depressive symptoms, low self-esteem,
and low life satisfaction and meaning in life. “Anxiety
symptoms”featured high anxiety; it also included low self-
esteem and above average use of negative coping strategies.
“Impulsivity”was marked by high impulsivity and diffi-
culties with attention and conduct behaviors. Factor load-
ings are available in Table 4. As expected, the correlation
between the three components was weak (highest correla-
tion r=.17, p<.001). In Step 2 described above, it
appeared that the benefits of increasing complexity dimin-
ished after the six-profile solution (Fig. 1). The six-profile
solution, therefore, was chosen to balance complexity and
efficiency. The mean component score for each profile gave
an overview of the psychological characteristics of each
profile (Fig. 2).
In Step 3 described above, we compared the percentage
within each profile who reported new six-month self-harm
among students who did not report recent self-harm at
baseline (Table 1). As expected, new self-harm varied
between the profiles, and rates appeared to vary in line with
degree of psychological pathology (1.4% (n=1) to 37.5%
(n=3)). The following section describes the psychological
profile in more detail and briefly describes the social and
environmental features of the groups, as described in Steps
3 and 4 above (Tables 1and 2).
Profile 1: Psychologically Healthy—1.4% New
Self-Harm
As evident in the psychological component scores in Table
1, this group (n=72) had less anxiety and better than
average use of coping strategies (higher on positive and
lower on negative strategies). The individual psychological
variables in Table 1also showed an overall healthy score
for this profile; low anxiety and high self-esteem were
standout scores. This was accompanied by the lowest rate of
Fig. 1 Euclidean distance for profile solutions 1 to 10: the benefits of
increasing complexity diminished after the six-profile solution
J Youth Adolescence
Table 1 Psychological profile of six profiles formed at baseline for the longitudinal sample (n=326) and new self-harm at follow-up among those who did not self-harm at baseline (n=269).
Statistics are mean (SD) or % (n)
1 Psychologically
‘healthy’(n=72)
2 Low impulsivity
(n=58)
3 Poor coping
(n=59)
4 Anxiety
(n=58)
5 Impulsive
(n=42)
6 Pathological
(n=37)
K-W or χ
2
pAverage
Psychological components
Anxious symptoms −0.80 (0.38)
2,3,4,5,6
0.27 (0.53)
1,3,4,6
−0.55 (0.47)
1,2,4,5,6
0.52 (0.48)
1,2,3,5,6
0.10 (0.56)
1,3,4,6
1.26 (0.51)
1,2,3,4,5
122.388 <.001 0.02 (0.82)
Poor coping −0.70 (0.41)
2,3,5,6
−0.26 (0.50)
1,3,4,5,6
0.59 (0.44)
1,2,4,5,6
−0.69 (0.45)
2,3,5,6
0.26 (0.62)
1,2,3,4,6
1.23 (0.66)
1,2,3,4,5,6
122.116 <.001 0.03 (0.85)
Impulsivity −0.26 (0.51)
2,4,5,6
−1.00 (0.40)
1,3,4,5,6
−0.28 (0.47)
2,4,5,6
0.21 (0.44)
1,2,3,5
1.26 (0.50)
1,2,3,4,6
0.14 (0.73)
1,2,3,5
106.735 <.001 −0.07 (0.82)
Individual variables
Depression 5.1 (1.9)
2,3,4,5,6
6.4 (2.0)
1,6
7.1(2.4)
1,6
6.5 (2.3)
1,6
7.3 (2.5)
1,6
10.1 (2.9)
1,2,3,4,5
76.447 <.001 6.8 (2.7)
Anxiety 4.8 (2.1)
2,3,4,5,6
9.1 (3.7)
1,3,4,5,6
6.3 (2.7)
1,2,4,5,6
11.0 (3.3)
1,2,3,6
10.2 (3.1)
1,2,3,6
14.8 (2.6)
1,2,3,4,5
179.88 <.001 8.8 (4.3)
Self-esteem 24.2 (3.5)
2,3,4,5,6
16.4 (4.0)
1,6
17.4 (3.4)
1,6
17.1 (3.7)
1,6
15.8 (3.5)
1,6
7.4 (3.7)
1,2,3,4,5
186.781 <.001 17.3 (5.9)
Attention difficulties 3.8 (1.8)
3,4,5,6
3.5 (1.9)
3,4,5,6
4.7 (2.1)
1,2,4,5,6
6.6 (2.0)
1,2,3,5
7.8 (1.5)
1,2,3,4,6
6.7 (2.0)
1,2,3,5
136.901 <.001 5.3 (2.5)
Conduct difficulties 2.0 (1.9)
4,5,6
1.3 (1.4)
3,4,5,6
2.4 (1.7)
2,4,5,6
3.2 (1.9)
1,2,3,5
6.0 (2.1)
1,2,3,4,6
3.7 (2.3)
1,2,3,5
104.457 <.001 2.9 (2.3)
Impulsivity 2.1 (0.4)
2,5,6
1.6 (0.3)
1,3,4,5,6
2.1 (0.4)
2,4,5,6
2.2 (0.4)
2,3,5
2.9 (0.4)
1,2,3,4,6
2.3 (0.6)
1,2,3,5
141.398 <.001 2.2 (0.5)
Positive coping 3.6 (0.4)
2,3,5,6
3.4 (0.5)
1,3,4,5,6
2.4 (0.5)
1,2,4,5
3.6 (0.4)
2,3,5,6
2.7 (0.6)
1,2,3,6
2.1 (0.7)
1,2,3,4,5
184.505 <.001 3.1 (0.8)
Negative coping 2.0 (0.5)
2,4,5,6
2.4 (0.5)
1,3,4,5,6
2.1 (0.5)
2,4,5,6
2.9 (0.6)
1,2,3,6
3.0 (0.7)
1,2,3,6
3.4 (0.7)
1,2,3,4,5
144.237 <.001 2.5 (0.7)
Meaning in life 4.5 (0.5)
2,3,4,5,6
4.1 (0.7)
1,3,5,6
3.3 (0.9)
1,2,4,6
4.1 (0.7)
1,3,5,6
3.0 (1.0)
1,2,4,6
2.1 (0.9)
1,2,3,4,5
140.843 <.001 3.7 (1.1)
Life satisfaction 4.9 (0.9)
2,3,4,5,6
4.0 (1.0)
1,3,5,6
3.5 (1.2)
1,2,4,6
4.0 (1.1)
1,3,5,6
3.0 (1.2)
1,2,4,6
1.6 (1.0)
1,2,3,4,5
151.053 <.001 3.7 (1.4)
Six-month self-harm at baseline (n=326)
Baseline: None 98.6% (71)
3,6
91.4% (53)
6
84.7% (50)
1,6
87.9% (51)
6
85.7% (36)
6
21.6% (8)
1,2,3,4,5
139.013 <.001 82.5% (269)
Occasional 1.4% (1)
3,6
8.6% (5)
6
15.3% (9)
1,6
10.3% (6)
6
9.5% (4)
6
40.5% (15)
1,2,4,5
12.3% (40)
Repetitive O
6
O
6
O
6
1.7% (1)
6
4.8% (2) 37.8% (14)
1,2,3,4,5
5.2% (17)
Six-month new self-harm at follow-up among those who did not self-harm at baseline (n=269)
New self-harm at follow-up 1.4% (1)
4,5,6
7.5% (4)
6
8.0% (4) 9.8% (5)
1,6
13.9% (5)
1
37.5% (3)
1,2,4
15.265 0.009 8.2% (22)
J Youth Adolescence
six-month occasional self-harm and no adolescents in this
group reported repetitive self-harm. The social and envir-
onmental description of this group was similarly unre-
markable (Table 2). As a group, these adolescents reported
good support from family, friends, and adults at school. It is
worth noting that this group reported the lowest level of
self-harm modeling from friends: 16.7% reported having a
friend who self-harmed compared with the group average of
30.1%. This was the largest profile. At six-month follow-up,
there was only one case of new self-harm.
Profile 2: Low Impulsivity—7.5% New Self-Harm
This profile (n=58) appears psychologically healthy, with
scores for most psychological variables similar to the
average. The only defining feature of this profile was a low
score on the psychological component Impulsivity, and
corresponding low impulsivity on the individual variables.
This profile was significantly lower on the impulsivity
component scores than the other five profiles, as evidenced
by the pairwise comparisons. The social and environmental
features largely reflected the averages for the whole sample,
with notably high support from family and friends. Within
the 53 without self-harm at baseline, four reported self-harm
at follow-up.
Profile 3: Poor Coping and Low Anxiety—8.0% New
Self-Harm
Component scores for this profile (n=59) indicated below
average use of positive coping strategies and lower than
average anxiety. On the impulsivity component this profile
was mid-range: higher than the low impulsivity profile
(Profile 2) but lower than Profiles 4, 5 and 6. The social and
environmental features on the whole reflected the averages
for the sample, although support from family and friends
was lower than that reported by Profiles 1 and 2. Among the
50 without self-harm at baseline, four reported self-harm at
follow-up.
Profile 4: High Anxiety—9.8% New Self-Harm
Anxiety was slightly high in Profile 4 (n=58); higher than
in all profiles except the comparatively pathological Profile
6. The scores for positive and negative coping strategies
were slightly above average. Social and environmental
features of this profile were largely mid-range, apart from
self-harm modeling from friends: it was much higher in this
profile, on par with the highest level among all profiles
(43.1%). Of 51 who did not report self-harm at baseline,
five reported self-harm at follow-up.
Profile 5: Impulsive—13.9% New Self-Harm
Profile 5 (n=42) was marked by high impulsivity on the
psychological component scores. This was reflected in the
individual variable scores: high impulsivity, and difficulties
with attention and conduct. The standout social feature of
this profile was high scores on both bullying others and
being a victim of bullying. This profile had the highest
percentage of males. Of 36 who did not report self-harm at
baseline, five reported self-harm at follow-up.
Profile 6: Psychological Pathology—37.5% New Self-
Harm
Profile 6 (n=37) was the smallest group, and the psycho-
logical component scores revealed high levels of anxiety
and difficulty coping. This was corroborated in the indivi-
dual psychological variables, where we saw high depressive
and anxiety symptoms, low self-esteem, low levels of
positive coping strategies, high use of negative coping
strategies, and low meaning in life and life satisfaction. The
social and environmental profile in Table 2added to the
picture with the lowest levels of support from family,
Fig. 2 Component mean scores
for adolescents: average of the
whole sample (far left) and the
six profiles. The shaded area
indicates +/−0.5 SD, the
expected variation of normal
scores. The vertical lines at each
mean indicate standard error
J Youth Adolescence
friends, and adults at school, and the highest score on victim
of bullying experiences. This profile was female dominated
and had a lower percentage of biological parents married.
This profile reported the highest levels of occasional
(40.5%) and repetitive (37.8%) self-harm at baseline, along
with the highest level of new self-harm at follow-up (37.5%
of the eight without self-harm at baseline).
Five- and seven-cluster solutions were also considered,
and while the profiles must necessarily differ in detail, they
were not fundamentally different from those reported in the
six-cluster solution in this article. For example, the five-
cluster solution yields similar profiles, however the six-
cluster solution offers greater clarity regarding scores for
impulsivity.
Discussion
Adolescent self-harm is common, but poses concerns for the
teen’s physical safety, general mental health, and the
potential for contagion among peers (O’Connor et al. 2009;
Taliaferro and Muehlenkamp 2014). Past research identifies
a number of risk factors for adolescent self-harm, but much
is yet to be understood regarding the mechanisms and
interactions at play (Hawton et al. 2012). An important
limitation in past research is that research typically
approaches risk factors as distinct components. Yet we
know that there is considerable overlap between many risk
factors (e.g., depression and anxiety). Therefore in recent
years, a small contingent of self-harm research has adopted
a profile approach to consider multiple overlapping factors
simultaneously and identify distinct groups within those
who report self-harm (Somer et al. 2015). This study
focused on psychological risk factors for self-harm and
assigned individuals to groups based entirely on psycholo-
gical profiles, as in limited prior cross-sectional research
(Stanford and Jones 2012). This study extended prior
research by exploring how these profiles related to self-
harm behavior at follow-up. We hypothesized that profiles
with poorer psychological function at baseline would be
associated with higher rate of new self-harm at six-month
follow-up, as found in past cross-sectional research (Somer
et al. 2015; Stanford and Jones 2012).
Australian high school students (n=326, 68.1% female)
completed a questionnaire including a broad range of psy-
chological and socioenvironmental risk and protective fac-
tors. Non-hierarchical cluster analysis produced six groups
with different psychological profiles at baseline and rate of
new self-harm at follow-up. Overall six-month self-harm
prevalence was 12.3% for occasional self-harm and 5.2%
for repetitive self-harm. This is broadly in line with rates in
other community samples (Stallard et al. 2013). The lowest
rate of new self-harm was 1.4% in the psychologically
Table 2 Social and environmental scores for the six profiles. Statistics are mean (SD) or %(n)
1 Psychologically
‘healthy’(n=72)
2 Low impulsivity
(n=58)
3 Poor coping
(n=59)
4 Anxiety
(n=58)
5 Impulsive
(n=42)
6 Pathological
(n=37)
K-W or χ
2
pAverage
Age mean (SD) 14.1 (1.3) 14.2 (1.4) 13.8 (1.4) 14.1 (1.5) 14.0 (1.6) 14.2 (1.4) .919 .469 14.1 (1.4)
Female % (n) 62.5% (45) 77.6% (45) 59.3% (35) 77.6% (45) 50.0% (21)
6
83.8% (31)
5
18.460 .002 68.1% (222)
Parents married % (n) 80.6% (58) 86.2% (50) 84.7% (50) 82.8% (48) 78.6% (33) 70.3% (26) 4.651 .460 81.3% (265)
Born overseas % (n) 4.2% (3) 8.6% (5) 13.6% (8) 5.2% (3) 9.5% (4) 18.9% (7) 8.862 .115 9.2% (30)
Supportive family 5.6 (0.9)
2,3,4,5,6
5.2 (1.2)
1,3,4,5,6
4.5 (1.5)
1,2
4.7 (1.4)
1,2,5,6
4.1 (1.4)
1,2,4
3.5 (1.8)
1,2,4
15.511 <.001 4.7 (1.5)
Supportive friends 4.9 (1.2)
3,4,5,6
4.6 (1.3)
3,4,5,6
3.7 (1.5)
1,2
4.0 (1.5)
1,2,6
3.7 (1.3)
1,2,4,6
2.8 (1.9)
1,2,4,5
13.515 <.001 4.1 (1.5)
Supportive adult at school 4.6 (1.5)
2,3,4,5,6
4.1 (1.5)
1,5,6
3.8 (1.5)
1
3.7 (1.7)
1,2,5,6
3.0 (1.6)
1,2,4
2.8 (1.7)
1,2,4
9.337 <.001 3.8 (1.7)
Self-harm modeling: friends 16.7% (12)
4,6
32.8% (19) 22.0% (13) 43.1% (25)
1
31.0% (13) 43.2% (16)
1
15.920 .007 30.1% (98)
Self-harm modeling: family 1.4% (1) 10.3% (6) 10.2% (6) 13.8% (8) 4.8% (2) 10.8% (4) 8.421 .135 8.3% (27)
Victim of bullying 1.7 (1.4)
2,4,5,6
2.5 (1.7)
1,6
2.4 (2.0) 2.8 (2.4)
1,6
3.2 (2.4)
1
3.9 (2.6)
1,2,4
6.777 <.001 2.6 (2.1)
Bully others 0.21 (0.6)
5,6
0.3 (0.8)
5,6
0.3 (0.6) 0.4 (1.0)
5,6
1.4 (1.4)
1,2,4,6
0.9 (1.4)
1,2,4,5
11.178 <.001 0.5 (1.0)
Importance of faith 3.2 (0.8)
2,3,5,6
2.9 (0.8)
1,3,5,6
2.5 (0.7)
1,2,4
3.0 (0.9)
3,5,6
2.5 (0.8)
1,2,4
2.3 (1.0)
1,2,4
10.577 <.001 2.8 (0.9)
Positive religious coping 3.2 (0.8)
3,5,6
3.0 (0.8)
3,5,6
2.4 (0.9)
1,2
3.0 (0.9)
5,6
2.5 (1.0)
1,2,4
2.2 (0.9)
1,2,4
10.772 <.001 2.8 (0.9)
Negative religious coping 1.6 (0.7)
3,4,5,6
1.8 (0.8)
4,5,6
1.9 (0.8)
1
2.1 (0.9)
1,2,6
2.2 (0.9)
1,2,6
2.7 (1.1)
1,2,4,5
9.619 <.001 2.0 (0.9)
J Youth Adolescence
“healthy”profile; total self-harm across both time points for
this group was 2.8%. This group appeared psychologically
healthy, with good use of coping strategies and low anxiety.
At the other end of the spectrum, the highest rate of new
self-harm was 37.5% in the “pathological”profile; 86.5% of
the pathological profile reported self-harm at either time
point. This group appeared to have multiple difficulties,
with scores indicating high anxiety and poor use of coping
strategies. This concurs with prior work identifying greater
psychological pathology in groups with higher self-harm
rates, a common finding across studies creating profiles
based on psychological (Stanford and Jones 2012) and self-
harm (Somer et al. 2015) characteristics. An understanding
of the highest risk profile for self-harm may assist teachers
and counsellors in detecting those who are the highest
priority for treatment (Somer et al. 2015) and at greatest risk
for future self-harm.
In between these two endpoints, new self-harm was
around average for the remaining four profiles (7.5% to
13.9%). The psychological scores for these four profiles
were varied, and suggest a group with low impulsivity, a
group with low anxiety but below average use of positive
coping strategies, a group with mild anxiety but good use of
positive coping strategies, and an impulsive group. This
concurs with past research suggesting that there is no single
profile to describe adolescents who self-harm (Stanford and
Jones 2012), and therefore we require a more complex
approach to understanding risk factors. Each group of
adolescents may have different prevention and intervention
needs. For example, while some adolescents may need
assistance with coping strategies, others need help with
anxiety, and still others need to bolster their ability to
manage impulsive tendencies when faced with the desire
to self-harm. Thus there is no “one size fits all”approach to
preventing and reducing self-harm in community
adolescents.
The results of this study concur with and extend prior
research into psychological risk factors for self-harm. For
example, past research has identified combined bully-victim
status as a stronger risk factor for self-harm compared with
either bully or victim status independently (Barker et al.
2008). In our sample, this combination was primarily evi-
dent in the impulsive profile—the group with the highest
score on bullying others. The “pathological”profile also
displayed this combination more subtly. Where previous
research has identified bully-victim status as a strong risk
factor for self-harm in general, our study gives insight into
one subgroup in which this this risk factor is prominent. As
another example, past research has identified that coping
strategies are associated with self-harm (De Leo and Heller
2004; Hall and Place 2010; Lewinsohn et al. 1994). How-
ever, non-significant findings also exist (O’Donnell et al.
2004). In our results, below average use of positive coping
strategies was evident in two out of the six profiles. These
two profiles had different rates of new self-harm, with
higher self-harm in the “pathological”profile in which poor
coping was accompanied by elevated depressive and anxi-
ety symptoms, and lower self-esteem. It would be difficult
to capture these nuances in a typical predictive or cumula-
tive risk model. Therefore there is a need for more complex
models such as a profile approach.
A key finding in our work is identifying a group of
adolescents who were average on all psychological mea-
sures (apart from low impulsivity) who reported new self-
harm (7.5%) at a rate on par with the average for the whole
sample (8.2%). These adolescents did not appear to
experience above average difficulties in psychological
domains, coping strategies, or relationships. Around one-
third of this group reported awareness of self-harm among
friends, which is in line with the average for the sample. It is
beyond the scope of this study to investigate the role of
social contagion in each group, but this flags an important
area for future research (Jarvi et al. 2013). The absence of
typical psychological self-harm risk factors in this group
confirms the need to move beyond a single list of self-harm
risk factors. In community settings, these adolescents may
not be identifiable through any known risk factors. This is
concerning, given that 25–65% of those who self-harm do
not disclose the behavior to anybody (Armiento et al. 2014;
Madge et al. 2008; Rubenstein et al. 1998). Therefore
policies to respond to and reduce self-harm need to be
designed with hidden behavior in mind.
Reflecting on the heterogeneity of adolescents who self-
harm, we suggest three strategies for self-harm prevention
and intervention in schools: screening, gatekeeper training,
and mental health programs. These programs are designed
to operate in addition to the existing student support sys-
tems (Juhnke et al. 2011).
Given the variability in, and indeed, absence of risk
factors identified, we recommend universal screening for
self-harm in schools. Initial research suggests that screening
is largely well received and does not cause undue distress;
however, further research is needed to ascertain sensitivity/
specificity and financial viability (Robinson et al. 2011).
Further research is also needed to better understand whether
distress occurs for any participants and develop strategies to
reduce potential distress (Hasking et al. 2015). However,
even if screening is effective, low-risk, and financially
viable in a cost-benefit analysis, schools may lack the
resources to undertake universal screening for all adoles-
cents. Where universal screening is not possible, we
recommend targeted screening. Bearing in mind the varia-
bility of self-harm risk factors, we recommend that school
counsellors use a brief mental health screening tool with all
clients or students, regardless of the reason for referral.
Screening should include a brief questionnaire, either
J Youth Adolescence
pencil-and-paper or, preferably, using an online platform to
maximize detection (Ougrin and Boege 2013).
Gatekeeper training aims to equip staff or student peer
leaders with skills to respond to students disclosing
self-harm and/or suicidal thoughts. While much of the
gatekeeper research centers on suicide prevention (e.g.,
Wasserman et al. 2015), evaluations of gatekeeper training
for self-harm appear promising. For example, training for
school welfare staff delivered by the Orygen Youth Health
service reported increased knowledge of, and confidence
and perceived skill in working with self-harm (Robinson
et al. 2008). Improvements were greater among those with
lower knowledge, confidence and skill at baseline. How-
ever, participants did not report reduced anxiety surround-
ing working with adolescents who self-harm. Future
research should include randomized controlled trials and a
broader range of outcomes including rates of self-harm,
staff anxiety, and improvements in practice. Research into
suicide prevention suggests that gatekeeper training is an
important component of the solution, although improve-
ments in skills, knowledge, and confidence may not trans-
late directly to reductions in suicide attempts or self-harm
(Wasserman et al. 2015; Wyman et al. 2008).
Mental health literacy and self-harm/suicide prevention
programs are designed to increase awareness of mental
health challenges and self-harm, reduce stigma, and
encourage help-seeking. Programs should be universal
where possible: prevention programs that target at-risk
adolescents are likely to miss a proportion of adolescents
with current or future self-harm, particularly those without
discernible psychological pathology. General mental health
literacy programs aim to reduce stigma and encourage help-
seeking. For example, preliminary evidence using a ran-
domized controlled trial suggested that the HeadStrong
program reduced stigma, but failed to increase help-seeking
behavior (Perry et al. 2014). Perry and colleagues suggest
that sustained education is needed to change help-seeking
behavior and maintain these effects. Mental health literacy
programs can also include “contact”, that is an interactive
session with a young person with lived experience of mental
illness. While potentially valuable, contact is yet to prove
efficacious in adolescent samples and further research is
needed (Chisholm et al. 2016). School-based self-harm
programs appear to be a promising strategy for universal
self-harm prevention (Robinson et al. 2016). However,
schools are often concerned regarding the potential for
iatrogenic effects when discussing self-harm. To address
these concerns, we need large randomized controlled trials
to review the positive and negative effects of programs on a
range of outcomes (Robinson et al. 2013). One such pro-
gram is the “Signs of Self-Injury”program. It is the only
universal self-harm program currently evaluated (Robinson
et al. 2016). Initial evidence for the program appears
promising, with no increase in self-harm thoughts, behavior,
or frequency (Muehlenkamp et al. 2010). To avoid iatro-
genic effects, discussions about self-harm should be framed
within broader mental health programs, with a large focus
on protective behaviors and strengthening resilience
(Juhnke et al. 2012; Knightsmith 2015; Robinson et al.
2016). Schools should also make students aware of avenues
for support online, as there is emerging evidence to suggest
that this may be less intimidating for adolescents and may
lead to seeking in-person professional support (Frost et al.
2015).
Finally, efforts should be made to build supportive
environments in which people are willing to disclose self-
harm, and where people know how to respond in safe and
supportive ways (Juhnke et al. 2012). It can be very difficult
to disclose self-harm. Barriers to disclosure include fearing
a negative response, concern that the disclosure would be
spread in the community, and not viewing self-harm as
problematic (Klineberg et al. 2013; Wadman et al. 2016).
When adolescents do disclose, they do not necessarily open
up to the school counsellor or a trained mental health pro-
fessional. In an adolescent community sample, students
were twice as likely to disclose to a peer rather than an adult
(Hasking et al. 2015). When disclosing to an adult, the most
common person was a parent rather than a mental health
worker or teacher. Disclosure to peers can cause concern
regarding the potential for “contagion”in schools (Jarvi
et al. 2013). Indeed, one-third of participants in this study
reported awareness of self-harm among peers. Therefore,
we need multifaceted mental health programs that reduce
stigma and empower all levels of the community.
This study has several strengths. For the first time, we
explored psychological profiles longitudinally in a com-
munity sample. There are two key advantages to the psy-
chological profiling approach. Firstly, we can consider
multiple overlapping variables simultaneously rather than
treating each variable as statistically independent. Secondly,
we can identify multiple groups with varying profiles.
Traditional risk factor models that create a single profile of
risk factors cannot account for this variability. By using a
longitudinal sample we were able to explore whether psy-
chological profiles identified at Time 1 were associated with
new self-harm at Time 2. Another strength of this study was
the inclusion of a broad range of risk and protective factors.
There were, however, several limitations. It was not
possible to cover all risk factors. The broad nature of the
project necessitated utilizing brief, self-report measures
which indicated symptomology; it would be good to clarify
these findings using diagnostic scales. Despite considerable
efforts to engage students in the research, using opt-in
parental consent contributed to a lower than ideal partici-
pation rate and may have reduced the sample’s representa-
tiveness. Although response rate varied depending on the
J Youth Adolescence
school’s effort in collecting parental consent, all participat-
ing schools communicated that opt-in parental consent was
very challenging to administer. Indeed, schools expressed
that they have difficulty obtaining parental consent for
activities with high desirability such as excursions. Further,
timetabling challenges in two schools impacted upon the
retention rate, as very few students in those schools were
able to participate. The constraints of this project only
enabled a six-month follow-up; therefore it was not possible
to investigate psychological profiles over the course of
adolescence. This study reports on data from fee-paying
Independent schools. While a comparison of each school’s
fees and location indicates considerable variability in
sociodemographic composition, future research should
include a broader sample range including public schools.
Future research in larger samples should explore profiles
for male and female adolescents separately, since the pro-
portion of males varied between groups. Randomized con-
trolled trials could explore the efficacy of universal and
targeted prevention programs focused on one or more of the
psychological risk factors identified, such as anxiety,
impulsivity, and coping strategies. Programs could also
target bullying, and strategies to improve supportive rela-
tionships. Program evaluation could consider whether
adolescents in different profiles respond differently to the
prevention or intervention strategy. It may be necessary to
support tailored program strategies with brief mental health
screening tools to enable efficacious program selection.
Future research can build on the current study by recruiting
larger samples and conducting longer follow-ups. This
study focused on new self-harm rates; larger scale studies
can explore trajectories and consider whether adolescent
profiles remain stable over time (Klonsky and Olino 2008).
This is an important question for profile research, given that
self-harm severity, method, and function changes over time
(Owens et al. 2015; Townsend et al. 2016; Wadman et al.
2016).
Conclusion
Past research identifies a number of risk factors for ado-
lescent self-harm, but often fails to account for overlap
between these factors. Thus the current understanding of the
complex interactions between risk factors is limited. This
study contributes to this knowledge gap by developing our
understanding of psychological risk factors for self-harm in
adolescence using a profile analysis. This article used psy-
chological profiling to explore complexity in self-harm risk
factors in a longitudinal adolescent community sample. We
identified six groups with distinct psychological profiles. As
expected, increased psychological pathology at baseline
was associated with higher rates of new self-harm at follow-
up. Notably, this study highlighted diversity in risk factors
for adolescent self-harm. We identified a number of groups
with similar self-harm rate that display disparate psycho-
logical profiles, including difficulties with anxiety, impul-
sivity, and coping strategies. Therefore adolescents who
self-harm cannot be accurately described using a single list
of risk and protective factors. A more complex under-
standing of the psychological risk factors for adolescent
self-harm may assist in detecting those who are at greatest
risk for future self-harm, and ultimately moving toward
prevention.
Author Contributions S.S. participated in writing, design, and
analysis, and carried out the data collection. M.J. contributed to
writing, design, and analysis. J.H. provided clinical guidance and
feedback on the manuscript. All authors read and approved the final
manuscript.
Compliance with Ethical Standards
Conflict of Interest The authors declare that they have no compet-
ing interests.
Ethical Approval The study was reviewed and approved by the
Macquarie University Human Research Ethics Committee, reference
number 5201400575.
Informed Consent The project was approved by the School Prin-
cipal, Executive and school counsellors in each school. Parents and
students provided opt-in informed consent at the first time point; this
consent covered the baseline and follow-up survey. At the follow-up,
parents were provided the full study information and the opportunity to
opt-out on behalf of their teen, and students again provided opt-in
informed consent.
Appendix
Table 3, Table 4
J Youth Adolescence
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Negative coping .634 .030 .371
Life satisfaction −.493 −.579 −.203
Meaning in life −.362 −.673 −.209
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Sarah Stanford recently completed her PhD Psychology at Macquarie
University. Her PhD focused on risk factors for and outcomes of self-
harm in adolescence and early-mid adulthood.
Professor Mike Jones is Associate Dean (Research) in the Faculty of
Human Sciences and Deputy Head of the Psychology Department at
Macquarie University. His primary research interests are in brain–gut
interactions. Together with Australian and international collaborators
he seeks to understand the mechanisms by which functional
gastrointestinal disorders are so strongly associated with a number of
psychological disorders and negative personality traits.
Professor Jennie Hudson is an Australian Research Council, Future
Fellow within the Centre for Emotional Health, Department of
Psychology. Jennie’s research focuses on understanding factors that
contribute to the development of anxiety disorders in children and
adolescents. Her work also involves the development and evaluation of
evidence based treatments for anxiety and depression in young people.
J Youth Adolescence
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