Protective and risk factors for social and emotional well-being of Indigenous children and adolescents: A rapid review
Bibliographic record
Abstract
Background: Indigenous children and adolescents experience life circumstances that significantly affect their social and emotional well-being (SEWB) and limit their capacity to fulfil their potential. This contributes to inequities in health, education, employment, and justice system involvement.Aim: We aimed to synthesise the existing literature to comprehensively understand the protective and risk factors for SEWB of Indigenous youth in Canada, Australia, New Zealand, and the United States (CANZUS).Methods: We conducted a systematic search of English literature using Google Scholar, Scopus, Informit, HealthInfonet, and PubMed.Results: Sixty-nine articles met the inclusion criteria. The identified risk and protective factors were mapped according to seven thematic and interconnected areas including connection to the (a) body; (b) mind and emotions; (c) family and kinship; (d) community; (e and f) culture, country, and land (place/space); and (g) ancestry and spirituality.Conclusions: Indigenous peoples’ perceptions of SEWB differ from traditional Western conceptualisations of health. Their perceptions carry a culturally distinct meaning, which is largely shared by Indigenous peoples across the CANZUS societies. An understanding of risk and protective factors for SEWB can inform targeted policy and public health practice frameworks aimed at improving Indigenous youth’s health and well-being.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".