Resilience‐Focused Approaches for School‐Age Australian First Nations Populations: A Systematic Review of Influential Factors
Bibliographic record
Abstract
INTRODUCTION: Building resilience has been identified as a key way to improve the wellbeing of children. However, there are currently no reviews of the evidence that explore factors influencing resilience in Australian First Nations School-age youth. OBJECTIVE: We aimed to review the literature on factors influencing resilience in school-age (5-19 years) Australian First Nations populations. We also explored how resilience is defined and operationalised, how factors identified mapped onto the Social and Emotional Wellbeing (SEWB) model and whether there were differences in factors depending on age and residential localities. DESIGN: We conducted a systematic review of published peer-reviewed articles that included the identification or review of factors influencing resilience in our target population. We searched key databases and performed a narrative synthesis. FINDINGS: Of the 1093 articles identified, 13 were found to meet inclusion criteria. Fifty-one different factors influencing resilience were identified across individual, interpersonal and community socio-ecological levels. DISCUSSION: The 51 factors mapped cohesively onto the SEWB domains. There was inconclusive data to determine if factors were dependent on the participants' age and location. Key limitations of the literature on this topic included the limited number of available studies and the lack of definitions and consistent operationalisation of resilience within the few existing studies. CONCLUSION: Our findings show the wide variety of factors that influence resilience in this population and demonstrate the importance of incorporating SEWB domains into wellbeing and resilience-focused programmes in Australian schools for First Nations populations.
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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 | 0.023 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".