School Connectedness Boosts Mental Health in Indigenous Adolescents With Adverse Childhood Experiences: Mediation Analysis of a Longitudinal Study in Australia
Bibliographic record
Abstract
BACKGROUND: The study examined whether school connectedness mediates the association between Adverse Childhood Experiences (ACEs) and mental health conditions among Indigenous adolescents, and if this mediation varies by school type-Public versus Private/Catholic METHODS: Using data from 13 waves of the Longitudinal Study of Indigenous Children (LSIC) in Australia (2008-2020), the present study examined the potential mediating effects of school connectedness in the association between exposure to ACEs and adolescent mental health conditions (anxiety/depression) in 636 Indigenous adolescents aged 12-17 years. Based on Baron and Kenny's approach, modified Structural Equation Modeling (SEM) techniques were employed to examine the mediating effect. All models were adjusted for covariates including age, sex, location, and socioeconomic position. RESULTS: The longitudinal analysis revealed that strong school connectedness and no/limited ACE exposure positively influenced mental health, regardless of school type (p < 0.05). Mediation analysis indicated that school connectedness significantly mediated the association between ACE exposure and mental health conditions for Indigenous adolescents who attended public schools (p < 0.05) but not for those who attended Private/Catholic schools. IMPLICATIONS FOR PRACTICE: These results underscore the critical role of school connectedness in supporting the mental health of Indigenous adolescents who have faced early childhood adversity. Notably, it highlights the unique needs of students in different school types and calls for further research to better understand how schools can foster well-being for Indigenous adolescents. CONCLUSION: Strengthening school connectedness offers a valuable avenue for promoting mental health among school-going Indigenous adolescents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".