School engagement and resilience in bullied indigenous adolescents: a strengths-based analysis of a longitudinal study
Bibliographic record
Abstract
Aim: The study aims to utilise a strengths-based approach to investigate whether Indigenous Australian adolescents with affective engagement in their schooling and education are more resilient, and if this resilience from affective engagement in school contributes to predict the negative consequences associated with bullying at school. Methods: The current study comprised 490 Indigenous adolescents aged 11–16 years from four Footprints in Time: The Longitudinal Study of Indigenous Children (LSIC) waves (W8, W10, W11 and W12 – conducted between 2014 and 2019) of the ‘Longitudinal Study of Indigenous Children’ dataset. In this study, Generalised Linear Models (GLMs) were employed to examine whether affective school engagement (independent variable) was associated with the outcome variable – the study child’s resilience (measured by the validated Strong Souls Resilience subscale) and whether it varied by child’s exposure to bullying victimisation. All models were adjusted for potential sociodemographic covariates (i.e. age, sex, location and socioeconomic position). Results: Of the 490 participants analysed, 89.4% ( n = 438) had high affective school engagement, 37.8% ( n = 185) were not bullied and the mean resilience score was 19.41 (SD = 5.21). Bivariate analysis revealed that there was a significant difference in median resilience score between two categories of school engagement ( p = 0.002). Longitudinal analysis using GLMs showed that high affective school engagement is a positive predictor of the study child’s resilience ( p = 0.013) compared with those with low school engagement. Affective school engagement was found to be associated with resilience only among those who were bullied compared with their counterparts ( p = 0.039). Conclusions: This study found that affective school engagement predicts resilience in Australian Indigenous adolescents. Affective school engagement may also serve as a protective factor for adolescents who have been bullied, potentially mitigating some of the harmful mental health outcomes linked with bullying. These findings underscore the potential for programmes that may promote affective school engagement in future initiatives to improve education inequities that cause health disparities for Indigenous peoples.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".