The Impact of Personal Background and School Contextual Factors on Academic Competence and Mental Health Functioning across the Primary-Secondary School Transition
Falkmer M (2014) The Impact of Personal Background and School Contextual Factors on Academic
Competence and Mental Health Functioning across the Primary-Secondary School Transition. PLoS ONE 9(3): e89874. doi:10.1371/journal.pone.0089874
The Impact of Personal Background and School Contextual Factors on Academic Competence and Mental Health Functioning across the Primary-Secondary School Transition
Torbjo rn Falkmer
Anne Elizabeth Passmore
Fiona Gillison, University of Bath, United Kingdom
Students negotiate the transition to secondary school in different ways. While some thrive on the opportunity, others are challenged. A prospective longitudinal design was used to determine the contribution of personal background and school contextual factors on academic competence (AC) and mental health functioning (MHF) of 266 students, 6-months before and after the transition to secondary school. Data from 197 typically developing students and 69 students with a disability were analysed using hierarchical linear regression modelling. Both in primary and secondary school, students with a disability and from socially disadvantaged backgrounds gained poorer scores for AC and MHF than their typically developing and more affluent counterparts. Students who attended independent and mid-range sized primary schools had the highest concurrent AC. Those from independent primary schools had the lowest MHF. The primary school organisational model significantly influenced post-transition AC scores; with students from Kindergarten - Year 7 schools reporting the lowest scores, while those from the Kindergarten - Year 12 structure without middle school having the highest scores. Attending a school which used the Kindergarten - Year 12 with middle school structure was associated with a reduction in AC scores across the transition. Personal background factors accounted for the majority of the variability in post-transition AC and MHF. The contribution of school contextual factors was relatively minor. There is a potential opportunity for schools to provide support to disadvantaged students before the transition to secondary school, as they continue to be at a disadvantage after the transition.
Funding: This project was funded by a Doctoral scholarship provided by the Centre for Research into Disability and Society and the School of Occupational
Therapy and Social Work, Curtin University, Perth, Australia. It was part of a larger study that was awarded the 2007 Social Determinants for Health Research award
by Healthway Australia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
The issue: Transition from primary to secondary school
The transition from primary to secondary school has long been
acknowledged as an important change in the lives of most students
. Despite contextual variations in school systems, similarities
in the features of this transition exist . Typically, the secondary
school transition involves simultaneous changes in school
environments, relationships, and academic expectations [1,57].
Students in Western Societies, including Australia, negotiate the
school transition at a time in development when they are striving
to gain independence from their parents, establish a unique
identity [8,9], and gain approval and support from peers .
Adjusting to the changes associated with the secondary school
transition can be challenging. Unsuccessful negotiation may set
some students on a trajectory of diminishing returns, not only in
the short-term [11,12], but also years thereafter .
Effects of the secondary school transition on typically
developing students academic competence (AC) and
mental health functioning (MHF)
Current evidence on the effects of the secondary school
transition on AC (also referred to as academic performance or
academic functioning) and MHF in typically developing students is
mixed. Some studies suggest mean AC scores significantly decline
after an initial settling-in period [3,1315]. Not every student
experiences changes to the same extent, or even in the same
direction [16,17]. For example, less academically able students
have been shown to have poorer adjustment to new school regimes
[1,18]. When compared to girls, boys have been shown to be more
negatively affected by self-consciousness about AC, leading to
declines in self-esteem and problems with adjustment subsequent
to the transition . The observed variability in AC across the
transition has been attributed to various reasons including: study
design and measurement issues (i.e., type of study, timing of data
collection, stability and specificity of measurement tools used);
social reference group variance; structural and philosophical
differences between schools; and differences due to gender-role
identification and personality [3,15,16,1823]. The role that the
transition itself plays in the academic attainment dip is unclear,
since a causal link between the transition and subsequent
attainment has yet to be established .
Variability in student MHF is conspicuous within the transition
literature. For example in one Australian study (restricted to two
primary schools and one high school), the majority (55%) of
students reported stable psychological health; 20% had better
functioning, and 25% reported decreased psychological
functioning through the transition . Variability in MHF has also been
reported in the US setting, where the middle school structure is
common . For example, Chung and colleagues  found
students MHF (from years 5 to 6) followed three trajectories
(average start to high; low to moderately high; consistently high).
Those with worse MHF prior to transition tended to have more
adaptive difficulties after the transition when compared to their
peers. Other studies report students with certain mental health
conditions to be at a greater disadvantage across this transition.
For example, studies suggest victimization is strongly related to
depression, and weakly related to anxiety , while others
suggest students with problem behaviours (disruptive or aggressive)
have greater problems adjusting to junior high school [1,29].
Gender differences in MHF have been documented. Girls report
more internalising  and anxiety problems , while boys
appear to exhibit more externalising problems [24,32]. Overall,
MHF across the primary-secondary transition varies widely
amongst typically developing students. While some view the
transition as demanding, others thrive on the challenges that it
creates . Therefore, considering a student cohort to be
homogenous could be misleading.
Limited focus on the impact of primary-secondary school
transition on AC and MHF of students with disabilities
Few studies have considered the impact of transition to
secondary school on AC and MHF of students with disabilities
. Students with learning disabilities have been reported to
experience reductions in AC , while their typically developing
counterparts show increased scores. Information on MHF of
students with disabilities is variable, and depends on the construct
used to define mental health. For example, when defined in terms
of self-esteem, studies reported those with special educational
needs (SEN)  and specific learning difficulties  to be at no
greater risk than their peers. Students with special educational
needs have, however, been reported to be at higher risk of bullying
in secondary school (with 37% out of 110 reporting to be bullied
when compared to 25% of their peers without SEN) . In
another study , while teachers reported students with specific
learning difficulties to have significantly more internalising and
externalising difficulties than their peers; students self-reported to
have significantly fewer problems. Thus, measurement issues
appear to confound the accuracy of self-reported mental health
data for the disability subgroup .
Effect of school contextual variables on AC and MHF
Although not explicit to the primary-secondary transition, there
is inconclusive evidence on the influence of school contextual
factors on AC and MHF. Various factors such as school size;
sector; organisational system; and school socio-economic status
(SES), which is based on the post-code of the school, have been
implicated . The contribution of school contextual factors
to post-transition functioning in a mainstream Australian sample
Australian studies on the primary-secondary school
Case studies and literature reviews dominate the Australian
literature on primary-secondary school transition [23,4552]. The
available deductive studies are constrained by small sample size,
design (i.e., convenience sampling) or scope (i.e., predominantly
focus on mental health, bullying, or the changes in the school
environment) issues that limit the generalisation of their findings
[24,33,50,53,54]. With too few schools involved in transition
research in Australia, exploration of school effects on student AC
and MHF across this transition is difficult (Fitz-Gibbon 1996;
Smyth 1999). Similarly, for students with disabilities there are no
Australian or international studies that have considered the impact
of secondary school transition on perceived AC and MHF, despite
inclusion of these students in the regular school system for decades
. The limited number and scope of studies precludes
speculation on whether this subgroup may be additionally
disadvantaged across the transition, even though they are at lower
baseline level to their typically developing counterparts before the
Aims and objectives
The overall aim of this study was to explore and compare
perceived AC and MHF of students with and without disability, six
months before and six months after transition to secondary school.
The objectives were:
N determine the unique and combined effects of personal
background factors (i.e., gender, disability and SES) on
academic competence and mental health functioning before
and after transition to secondary school;
N examine the added contribution of school contextual factors
(i.e., sector, organisational structure, mean-school SES) on
academic competence and mental health functioning before
and after transition to secondary school, after accounting for
personal background factors;
N determine the contribution of personal background factors and
school contextual factors on change in academic competence
and mental health functioning across the transition.
The current study is part of a larger study on the factors
associated with student academic, social-emotional and
participatory adjustment across the primary-secondary school transition
A cohort study using a prospective, longitudinal design with two
data collection points was used [Time 1 (T1) and Time 2 (T2)].
Informed written consent was obtained from school principals,
parents, teachers, and written assent was obtained from students to
participate in this study. In situations where the student declined to
participate, even with parental consent, they were not included.
All participants were made aware that they were not obliged to
participate in the study, and were free to withdraw from the study
at any time without justification or prejudice. At all stages, the
study conformed to the National Health and Medical Research
Council Ethics Guidelines . Full ethics approval was obtained
from Curtin University Health Research Ethics Committee, in
Western Australia (WA) (Reference number HR 194/2005).
The following inclusion criteria were applied to recruit students
into the study:
1. Attending a regular school in the educational districts of
metropolitan Perth or other major city centres of WA; and
2. Enrolled in the final year of primary school in WA (class 6 or 7)
in the academic years commencing January 2006 or 2007, and
due to transition to either middle or secondary school in
January 2007 or 2008. Further details on the schooling system
in WA are presented in Appendix A.
Students were categorised as having a disability if they were
reported to have medical diagnosis or a disability or a chronic ill
health condition, and were identified by their
parent(s)/caregiver(s) to be attending a regular class for the majority of their
weekly schooling hours (over 80% of the school hours per week),
with support provided as required. Thus, a broad definition was
used to define disability which entailed any addition medical
health condition that had the potential of impact on an
individuals daily functioning.
Several recruitment strategies were used to maximize reach and
1. A pre-paid package (containing poster, letter of invitation, and
school sector endorsement letter) was mailed out to principals
from 250 primary schools listed on the Department of
Education and Training, WA website. Schools listed in the
Canning, Fremantle-Peel, Swan, and West Coast educational
districts of Perth and major centres of Albany, Bunbury,
MidWest, Midlands, and Esperance educational districts of WA
2. A structured procedure was followed; with principal, teacher,
parent and student consent obtained in that order.
3. A poster and a letter of invitation were circulated to the
Disability Services Commission (DSC), the chief government
body offering services to school-children with disability in WA.
The DSC also posted a link to the study on its web page.
4. A pre-paid package (containing poster, letter of invitation,
school sector and DSC endorsement letters) was circulated via
known service providers, consumer groups, support groups,
families of students with a disability via individual providers,
and to any individuals who expressed interest in the study. In
order to over sample the disability subgroup, additional
snowball sampling occurred via participants forwarding
information to friends and family.
T1 data collection was timed to ensure that parents had a
definitive letter of acceptance from the secondary school, so that
the identified secondary schools could be contacted at the
commencement of the following academic year. The T1 parent
questionnaire requested parents to list the name of the secondary
school they planned to send their child to, for follow-up purposes.
Follow-up of participants was carried out using the above
mentioned recruitment procedure. T2 data collection commenced
6-months after the transition (Terms 3 and 4), after students had
passed through the short-lived variability in functioning due to the
transition, and had time to experience the new environments
which either supported or hindered their transition.
Data were collected via questionnaires, primarily paper and
pencil format. T1 data collection commenced in the second
semester (Terms 3 and 4) of the final year in primary school (class
6 or 7) in the academic years commencing January 2006 or 2007.
At T1, data from students, a parent (or primary caregiver) and the
main class teacher were collected. To ensure consistency of
administration, all questionnaires were administered on site by the
first author or research assistants. Administration guidelines were
developed to minimize administration bias. Student questionnaires
were designed to be completed within one sitting during their
regularly scheduled class time (3540 minutes). On completion of
this questionnaire, students returned it to staff and were given the
pre-coded parent questionnaire in a reply-paid envelope, to take
home. In cases where students were absent on the date of data
collection, parent and student questionnaire packages
(questionnaires and administration guidelines) were mailed-out to their
residence. At T1, data from 395 students from 75 primary schools
were collected. There were no more than 30 (11.3%) absent
students across the schools sampled, across both academic years.
Routine follow-up protocol for parent/student/teacher
questionnaires included: phone call to residence within two weeks;
reminder mail if questionnaires were not returned within four
weeks; and at least two fortnightly reminder phone calls.
T2 questionnaire administration commenced 6-months after the
transition to secondary school. Administration was undertaken in
the usual class times. Given that this was the second exposure to
the survey, a decision was made to mail out 40% of the parent and
student questionnaires to the students residence, with the
administration guideline and reply-paid envelope enclosed in the
package. At T2, data from students and the same parent (primary
caregiver) were collected. A student attrition rate of 32.7% resulted
in a T2 sample of 266 participants from 152 secondary schools
For the purpose of sample size estimation, it was assumed that
there would be approximately 10 independent variables in the
final regression model (for AC or MHF). In order to have power of
.90 (b = 0.1) and with a-value of .05 (type I error), a sample size of
215 would be required to detect a small to moderate effect size of
0.1 (Sample Size Program: PASS) . With an a-value of .05 and
a b of .2, any between group comparisons with the smallest of
groups, viz.: the 69 children with disabilities, allowed for a Cohens
d of .47 or larger to be detected.
Data collection instruments
AC. Items from the scholastic competence domain of the
SelfPerception Profile for Adolescents (SPPA) were used to measure
students perception of their AC (Harter, 1988). The SPPA has
comparable internal consistency in populations of students with
learning disability (a = 0.89), and behavioural disorders (a = 0.85)
. Considerate convergent, discriminant, and construct validity
of the academic competence scale in an equivalent US and
Australian sample has been substantiated . Higher scores
indicate better perceived AC.
MHF: The Strengths and Difficulties Questionnaire
(SDQ). The parent version of the Strengths and Difficulties
Questionnaire (SDQ) was used to assess students overall MHF
. The overall scores were computed by summing hyperactivity,
emotional symptoms, conduct problems and peer problems
subscales . Moderate to high internal consistency values have
been reported (a = 0.700.80) . Empirical studies supported
the tools discriminate and predictive validity [62,65]. The SDQ
score correlates strongly with the Child Behaviour Checklist 
but is more sensitive in detecting hyperactivity, and equally
effective in detecting internalising and externalising problems in
children and adolescents . Australian norms have been
published for the SDQ . Higher scores indicate lower MHF.
Family demographics and school contextual
characteristics. Family demographics: Items were drawn from
the Indicators of Social and Family Functioning Instrument
Version-1 (ISAFF)  and Australian Bureau of Statistics (ABS,
2001) surveys. Parents reported details on the family demographic
characteristics, residence post code, and childs disability.
Information on the school sector, post code number of students enrolled
in each school, and organisational structure at each school was
obtained from Department of Education and Training, WA
records. The school post code was used to calculate its
socioeconomic index (SEIFA Index), using the Commonwealth
Department of Education, Employment, and Workplace Relations
measure of relative socio-economic advantage and disadvantage
. In this study, the SEIFA decile was used as the measure of
mean school-SES, with a lower decile number meaning that the
school was located in an area that is relatively more disadvantaged
than other areas.
Data were managed and analysed using Statistical Package for
Social Sciences Version 20 and Statistical Analysis Software
Version 9.2. Data from the 2006 and 2007 cohort were alike on all
factors. Hence, for purposes of subsequent analyses, sample scores
were combined. Skewness/kurtosis measures indicated reasonable
symmetry. Only 1.82.5% of data were missing at scale levels. The
estimation maximization algorithm and Littles chi-square statistic
identified data to be missing completely at random, with the
probability level set at 0.05 [68,69]. Standard guidelines
recommended by the SDQ developers were followed to replace missing
values and sensitivity checks were undertaken to substantiate the
validity of data substitution techniques employed. Dummy
variables were created to represent the categorical personal
background and school contextual factors (i.e., independent
variables) incorporated into the regression models .
The General Linear Model (GLM) procedure was used to
address the studys objectives. The model was first tested with all
personal background factors (i.e., gender, disability and SES) and
their interactions. Since none of the interactions were statistically
significant, they were removed from the model. The most
parsimonious models including personal background and school
contextual factors for each outcome at T1 and T2 are presented.
The results from the model include the R2 or the proportion of
variance in the outcome variable that could be explained by each
personal background factor; the unstandardized regression
coefficients (B) and their standard errors (SE), and the Least-Square
(LS) means (or estimated population marginal means), which are
within-group means appropriately adjusted for the other effects in
the model .
At T1, data from 395 students from 75 primary schools were
collected. Mean age of the students at T1 was 11.89 years
(SD = 0.45 years, median = 12 years). A student attrition rate of
32.7% resulted in a T2 sample of 266 participants from 152
secondary schools. Chi-square and paired sample t-tests
demonstrated that the participants who continued to be involved in the
study at T2 did not differ from those who discontinued
involvement, on gender, disability, SES-level, AC and MHF.
The current paper presents data of the 266 students that answered
questionnaires at T1 and T2. Access to the complete data can be
obtained by contacting the first author.
Tables 1 and 2 give an overview of the key demographic
characteristics of the student sample. The majority of the students
in the disability subgroup had asthma, auditory disability, or a
learning disability. Seventy six percent (n = 203) of students were
from two-parent (original or biological) families, 11% (n = 29) were
from the blended/extended/combination families, while the
remaining 12.8% (n = 34) were from single-parent households.
English was the main language spoken in 95.5% households
(n = 252). Mothers of 23% (n = 60) of the sample did not have a
post-school qualification. Of those who had a post school
qualification, 5% (n = 13) completed a trade/apprenticeship
course, 31.5% (n = 82) completed a vocational education and
training certificate from college or Training and Further
Education, 20% (n = 52) had a bachelors degree, 20.4% (n = 53)
had a post graduate degree. Eighty-two percent of the mothers
(n = 218) were in paid employment, and 53.5% (n = 110) of the
working mothers held professional/managerial employment titles.
The remaining held clerical/administrative, technical, or sales
positions. T2 data were collected after 12 months. The mean age
of students at T2 was 12.9 years (SD = 0.57 years, median = 13
Of the 250 primary schools invited to participate in the study,
175 declined, resulting in a non-participation rate of 70%. Only
ten (14.9% of 67) students with disability were sourced from
outside the main school recruitment (through DSC and the
snowball). Details on the school characteristics of the 266 students
surveyed at T1 and T2 are presented in Tables 35.
At T1, 47% of the students (n = 125) were enrolled in the public
schools, 29% (n = 77) in Catholic schools and the remaining 24%
(n = 64) in independent/private schools. There was a movement
out of government schools towards Catholic and Independent
schools at T2, with 60% staying in the government sector while
over 85% of students in other sectors at T1 remained in those
sectors at T2. Almost 80% (n = 209) were in schools that followed
the K7K10/12 organisational system. The majority of the sample
at both T1 and T2 (T1 = 53.0%, n = 141; T2 = 45.1%, n = 120)
received their education from mid-range sized schools. Slightly
more than 90% (n = 240) moved to secondary school at the
Boy (Mean age 11.98 years, SD = 0.44 years)
Girl (Mean age 11.77 years, SD = 0.46 years)
No Disability (Mean age 11.84years, SD = 0.41 years)
Disability (Mean age 11.96 years, SD = 0.58 years)
Household SES-level [66,122]
$1599/per week (low-SES level)
$6001,999/per week (mid-SES level)
$2,000 +/per week (high-SES level)
completion of Year 7, and 79% (n = 211) moved to a secondary
school which was not connected with their primary school. Kappa
statistics was used to determine whether the agreement between
school sector attendance at T1 and T2 exceeded chance levels
. As shown in Tables 4 and 5, a significant change in school
sectors accessed by the total sample and sub-group with disability
across the transition was noticed (Kappa coefficient = 0.64).
Model predicting AC at T1
As shown in Table 6, personal background factors explained
14.2% of the variability in T1 AC scores. While the models
included a term for gender, this appeared not to be significantly
associated with either the AC scores at T1 or T2, or the change in
AC scores over the transition.
Students with disability reported significantly lower AC than
their typically developing counterparts. Household SES was
linearly related to T1 AC; with those from higher SES households
having the highest AC scores and those from socially
disadvantaged households having the lowest scores.
After accounting for personal background variability, school
contextual factors could explain an additional 3.1% of the
variability in T1 AC. In ascending order, students from Catholic
schools reported lowest AC, followed by government, and
independent sector students. Students attending larger schools
appeared to have lower AC scores than the other schools.
Model predicting AC at T2
At T2, personal background factors accounted for only 5.1% of
the variance in AC. The disability subgroup continued to report
lower AC than their typically developing peers (p = .0495), but the
magnitude of this difference was not as large as reported at T1.
The linear relationship between household-SES and T2 AC
continued (high-SES.mid-range SES.low-SES); but the strength
of this relationship reduced significantly.
After accounting for personal background variability, T2 school
contextual factors could not explain any additional variability in
T2 AC scores. T1 school size and organisational type continued to
account for 5.4% of the variability in T2 AC scores. Attending
large schools at T1 was associated with lower prospective AC (i.e.,
T2 AC). Students from T1 schools that used the K-7
organisational structure reported the lowest AC scores.
Model predicting change in AC over the
Personal background factors explained 5.2% of the change in
AC across the transition. Students with a disability showed an
improvement in AC compared with other students. Those students
who attended K-12 schools with middle school system appeared to
show a reduced AC score across the transition compared to
students from other school structure types.
Model predicting MHF at T1
At T1, personal background factors explained 21.4% of the
variability in MHF (Table 7). Boys and students with a disability
had lower scores than girls and students without disability
respectively. An inverse relationship between MHF and
household-SES was evident. In descending order, students from
higherSES households had better MHF than those from mid-range,
followed by low-SES. T1 School contextual factors could not
explain any additional variability in MHF than the
abovementioned personal background factors.
Model predicting MHF at T2
At T2, personal background variability explained 20.1% of the
variability in MHF. The difference in MHF between genders
narrows at T2 to the point that it is not statistically significant. The
students with a disability appeared to have significantly lower
MHF scores at T2 (similar to T1). The inverse linear relationship
between household SES and MHF persisted after the secondary
school transition. Similar to the T1 model, school contextual
factors could not explain any variability in MHF additional to the
personal background factors discussed above.
Model predicting change in MHF over the
Personal background factors accounted for 2.1% of the change
MHF across the transition, with none of them demonstrating
statistically significant associations. An unexpected finding was the
prospective impact of T1 school sector on change in MHF over
the transition (explaining 5.9% of the MHF change). Students who
attended independent schools at T1 reported lower MHF at T2,
while those who attended other school sectors showed small
improvements in their MHF.
Mixed evidence exists on the effects of this school transition on
student AC and MHF. Researchers generally agree that no given
student cohort is homogeneous [12,72]. By employing a
prospective longitudinal design, the current study examined the
contribution of personal background factors (i.e., gender, presence of
disability and household SES) and school contextual factors (i.e.,
size, sector, organisational model, and mean-SES) on perceived
AC and overall MHF across the primary-secondary transition.
School organisational structure
Primary school (K-7)
Secondary school (Y8-10/12)
K-12 without middle school
K-12 with middle school
Primary school size based on total
number of students
Secondary school size based on total
number of students
small = ,375
large = .975
small = ,700
large = .1250
Year of transition
Same secondary school as primary
1The SEIFA decile was used as the measure of mean school-SES, with a lower
decile number meaning that the school was located in area that is relatively
more disadvantaged than other areas.
Personal factors and AC and MHF at different times
across the transition
The current study found that personal background factors
explained the majority of the variability in student AC and MHF,
before and after the transition. A significant reduction in the
contribution of personal background factors on AC subsequent to
the transition, despite AC scores staying stable across time was
unexpected. This finding suggests that factors other than gender,
disability and household-SES influence AC at T2 . In the case
of MHF, the contribution of personal background factors
remained broadly constant at both times, a finding consistent
with previous evidence .
Students with a disability had lower AC than their typically
developing peers. This finding was in line with a number of
previous studies [35,58,74,75]. The reduced AC in the disability
subgroup could be explained by the negative social comparison
processes (referred to as the Big-fish-little-pond effect, (BFLPE)
. According to the BFLPE hypothesis, a students self-concept
is negatively correlated with ones peers. Thus, a students
academic self-concept depends not only on the students academic
accomplishments but also the accomplishments of those in the
school that the student attends. The consistently lower AC in the
disability sub-group found in the current study highlights the need
for schools to recognise and address this issue.
Of interest was an improvement in the disability subgroups AC
after the transition. This finding could suggest that there was a less
obvious BFLPE in secondary school, or the timing of data
collection which was 6-months after the transition to secondary
school was not long enough for ability groupings among students
to be obvious. Long term longitudinal studies that track students
through the secondary years of school would be beneficial in
understanding the effect of regular secondary school attendance
on the disability subgroups AC relative to their typical peers,
especially in light of evidence suggesting poorer school completion
rates and employment participation rates among youth and young
adults with disability [77,78].
The consistent poorer MHF in the disability subgroup found in
the current study could be attributed to several factors including
biological processes (e.g., deficits in cognition; language and
communication, social skills); the effect of medication; the
psychological burden associated with having a disability; or the
associations between mental disorders and lifestyle risk factors [79
86]. Given the importance of positive mental health in itself and
the detrimental impact of mental ill health on the individual and
society over time , the current studys findings reinforce
the importance of comprehensive, whole-of school, mental health
prevention programs currently operational in Australian primary
and secondary schools .
With regards to gender and AC, our studys findings support
egalitarian theories of the reduced gender-stereotyped socialization
over the past decade. This could be attributed to interventions and
legislation aimed at increasing girls motivation and participation
in academic pursuits. Future research into whether egalitarian
patterns hold in subject-specific academic domains (i.e., math,
Numbers in each cell show the number of students and percentage of the school sector to which they belong.
Numbers in each cell show the number of students and percentage of the school sector to which they belong.
computers, sciences, history) could help target specific
interventions for those most in need. In the case of MHF, a significant
gender bias favouring girls was evident in primary school. This
finding could be a function of poorer behaviour during the last
term of the school year (i.e., an effect of timing of measurement).
The improvement in overall MHF in boys after the transition and
the absence of any significant gender association with MHF in
secondary school is a positive finding. Our findings highlight the
need for primary and secondary schools to be equally sensitive and
responsive to the AC and MHF needs of boys and girls.
Consistent with earlier research [1,15], the detrimental effects of
social disadvantage on AC and MHF was evident. According to
the Family Investment Model (FIM), higher SES households can
afford to make significant capital investments in the development
of their children, while more disadvantaged families are forced to
invest in more immediate needs [98,99]. Economic deprivation
affects families well-being through an increase in family stress,
which in turn decreases ability to provide stability, adequate
attention, supervision, and cognitive stimulation to children .
The absence of any cumulative disadvantage of household-SES on
AC and MHF is optimistic. Furthermore, the reduced strength of
the association between social disadvantage and AC and MHF
post-transition, due to the improvement in the functioning of the
lower-SES group could be attributed to several factors, which
include: the transition trend noted in the study (i.e., increased
enrolment in independent schools); or the effect of measurement
(i.e., ceiling effect of the scales used, or the small sample size of the
low-SES group making the detection of significant differences
difficult due to power issues); or an indication that the transition to
secondary school is beneficial to the MHF and AC of students
from lower-SES subgroups. Nonetheless, this sub-group needs
support more than their more affluent peers.
School contextual factors and AC and MHF at different
times across the transition
Across the board, school contextual factors explained very little,
if any, of the variability in MHF, but more of the change in MHF
over time. These findings concur with past findings on the small
contribution of school factors on student MHF  and
relatively larger contribution on AC [105,106] and indicate that
most school contextual factors provide similar experiences  or
that school contextual factors are less important than personal
background factors on student AC and MHF. Furthermore, no
secondary school contextual characteristic (i.e., size, school sector,
organisational model, mean-school SES or their interactions)
influenced AC and MHF in secondary school. This means that
individual student factors and primary school contextual factors
are more important contributors of post-transition adjustment
than concurrent secondary school contextual factors. Thus, there
exists a greater responsibility on primary schools to ensure that the
transition needs of the disadvantaged groups are satisfactorily met.
To date, the effect of school sector (private or public) on student
outcomes is uncertain. Some findings suggest that that once
student-household SES is considered in the analysis, the advantage
of private schooling (independent schools) disappears or becomes
minimal [38,39]. Others suggest beneficial outcomes for those in
private education [40,108,109]. In the current study, we found
that even after accounting for personal background factors,
attending an independent primary school was associated with
higher concurrent AC but worse prospective MHF. The benefits
of independent school attendance on AC could be attributed to the
better resources, more functional and supportive school climate, or
fewer discipline problems noted in these schools . The lower
AC found amongst those who attended Catholic schools is an
unexpected finding which is contrary to past studies that highlight
the benefits of Catholic school attendance in terms of a steady
stream of funds that permits forward planning and budgeting, and
institutional autonomy .
A trend was noticed for the whole student sample including the
disability sub-group students to move out of government schools
into independent or private schools for secondary education. This
has been observed in previous Australian studies [41,42]. Despite
this transition trend, the absence of any significant contribution of
school SES (indexed by the SEIFA score) on AC and MHF after
adjustment for personal background factors validates the
applicability of whole of school mental health models across school
sectors, irrespective of social stratification. This finding is positive
and suggests that for our current sample, individual-household
SES was more important than the mean-school SES as far as AC
and MHF were concerned. Given the relative skewness of the
participating schools to higher deciles, it is likely that the detection
of significant differences was difficult due to power issues. Caution
ought to be exercised while interpreting these findings.
The sample of students was drawn from the Perth metropolitan
area and major city centres across WA, and did not involve
students from other rural and regional populations, or other major
metropolitan cities in Australia. Despite extensive recruitment
efforts, 70% of the schools declined to participate in the study,
which may have introduced a possible bias. The studys cohort
comprised 29% Catholic, 47% Government, and 24%
Independent schools, which was different to the profile of all schools in
Western Australia (15%, 72%, and 13% respectively) and may
limit the generalizability of the findings.
The majority of the students in the disability subgroup had
asthma, auditory disability, or a learning disability. The criteria for
inclusion into the disability category could have resulted in the
exclusion of students with more disability related physical,
Step2: School contextual factors
R2 = 9.8%
low-SES = ,$599
mid-range SES = $600$1999
high-SES = $2000+
T1 school structure
K-12 with middle school
K-12 without middle school
Primary school (K-7)
cognitive, social, and emotional restrictions . Thus, the
findings of the current study may underestimate the impact of
more severe disabilities on school transition. Statistically, it is also
likely that combining the reports of a heterogeneous disability
subgroup, with less disability related limitations, may have reduced
the sensitivity of the analyses . Additionally, we did not
account for the confounding effect of disability severity and
comorbidity status on AC and MHF . Replication of the
study findings in students from other school settings, such as
educational support units, separate schools that cater for students
with severe disabilities or students who were home schooled and
more severe disabilities is needed to extend generalizability.
In the current study, AC was evaluated by students only. Social
desirability self-report bias may have exaggerated the relationship
between the predictor variables and student perceived AC scores.
Parents reported on their childs overall mental functioning using
the SDQ, which tends to over emphasise externalising conduct
features. Especially during the adolescent years, it is likely that
children are more apt to have better insights into their own MHF
than their parents. Additional research that involves multisource
data from students, parents, teachers, and possibly clinical
interviews and school records, is warranted to validate these
Consistent with past studies , students from low-SES
households were under-represented in our sample. Despite small
numbers, the significant disadvantage found in this sub-group
suggest that these students are greatly disadvantaged (i.e., the true
effect size could be larger). We did not explicitly define the
subgroup of individuals from Indigenous and Torres Strait
communities due to ethical concerns. Further research is warranted to find
out whether the findings of this study can be generalised to all
subgroups of the Australian population.
Also, the two-point longitudinal study design did not permit us
study the longer-term effect of transition on AC and MHF. This is
an area worthy of scrutiny.
The current study is one of the few studies that investigated the
effects of personal background and school contextual factors on
AC and MHF across the primary-secondary transition, using a
student sample with and without disabilities. Our findings
highlight the existence of within-group variability in student AC
and MHF and the responsibility on primary schools to ensure that
the needs of disadvantaged groups are satisfactorily met, as these
students continue to be disadvantaged after the transition to
It is acknowledged that risks commonly accumulate and cluster
across multiple contexts of development [115,116]. Our findings
highlight the need for detailed, multi-contextual assessment of
personal background and school contextual factors that influence
student AC and MHF across the primary-secondary school
transition. Such studies are invaluable in guiding
transitionspecific interventions for all students in the regular school system.
Appendix A: The schooling system in WA
Schooling in Western Australia (WA) is delivered under the
States Education Act (1999), the Curriculum Council Act (1997)
and the Adelaide Declaration on National Goals for Schooling in
the Twenty-first Century (MCEETYA, 2004). The concept of
inclusion is firmly embedded within the WA Curriculum
WA has government (public) school and non-government
(private) school sectors. Government schools operate under the
direct responsibility of the State Minister of Education and
Training, and are represented by the Department of Education
and Training (DET). The non-government sector is represented
by the Catholic Education Office (CEO) and the Association of
Independent Schools (AISWA). One-third of all students in
Australia study in non-government schools, the majority of whom
are from middle and upper socio-economic status (SES)
background . WA government schools are all co-educational. The
privatised sector has co-educational and single-gender schools at
primary and at secondary level.
Predominately, a three-stage educational structure consisting of
pre-primary, primary, and secondary operates in most
government and non-government schools. Schools organisational
structures range from traditional primary-secondary school
configurations (Kindergarten Year 7, and Years 812), through separate
structures within larger frameworks from Kindergarten - Year 12
(K-12), to specially designated middle schools (Year 6/7-Year 8 or
10/12) . There are relatively few designated middle schools
in WA when compared to the US and the rest of Australia .
During the time of data collection for this study,
primarysecondary secondary school transition in WA occurred at the
Table 7. Personal background and school contextual factors associated with MHF at T1 and T2, and across the T1T2 transition
(higher value represents worse outcomes).
Step2: School contextual factors: Step 2:
No school variables contribute further
completion of Year seven (i.e., the year in which students turned
13). Post 2009, as part of a state-wide planning framework, a
phased relocation of Year 7 students into the secondary settings is
being undertaken on case-by-case .
Additionally, the models of inclusion for students with
disabilities adopted in schools across WA vary widely with regard
to student contact time in the regular classroom. In some inclusive
instances, students with disabilities who are based in regular
classrooms spend some time in specialised units or classes designed
to cater to their needs. Students with a chronic illness also spend
time out in hospital/home, or require assistance from nurses at
school. The term regular schools in this paper is used to refer to a
mainstreamed situation, in which students attend a regular class
for almost 80% of the school hours per week, with support from
specialised service providers offered as required.
Conceived and designed the experiments: SV AEP. Performed the
experiments: SV. Analysed the data: SV RP. Contributed reagents/
materials/analysis tools: SV AEP. Wrote the manuscript: SV TF RP MF
AEP. Critically reviewed the manuscript: TF RP MF.
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