Aspects of Wellbeing for Indigenous Youth in CANZUS Countries: A Systematic Review
Bibliographic record
Abstract
Indigenous children and young people (hereafter youth) across CANZUS nations embody a rich diversity of cultures and traditions. Despite the immense challenges facing these youth, many harness cultural and personal strengths to protect and promote their wellbeing. To support this for all youth, it is critical to understand what contributes to their wellbeing. This review aims to identify components contributing to wellbeing for Indigenous youth in CANZUS nations. Five databases were searched from inception to August 2022. Papers were eligible if they: focused on Indigenous youth in CANZUS nations; included views of youth or proxies; and focused on at least one aspect of wellbeing. We identified 105 articles for inclusion (Canada n = 42, Australia n = 27, Aotearoa New Zealand n = 8, USA n = 28) and our analysis revealed a range of thematic areas within each nation that impact wellbeing for Indigenous youth. Findings highlight the unique challenges facing Indigenous youth, as well as their immense capacity to harness cultural and personal strengths to navigate into an uncertain future. The commonalities of Indigenous youth wellbeing across these nations provide valuable insights into how information and approaches can be shared across borders to the benefit of all Indigenous youth and future generations.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".