Psychosocial factors associated with the mental health of indigenous children living in high income countries: a systematic review
Bibliographic record
Abstract
BACKGROUND: Indigenous children living in high income countries have a consistently high prevalence of mental health problems. We aimed to identify psychosocial risk and protective factors for mental health in this setting. METHODS: A systematic review of studies published between 1996 and 2016 that quantitatively evaluated the association between psychosocial variables and mental health among Indigenous children living in high income countries was conducted. Psychosocial variables were grouped into commonly occurring domains. Individual studies were judged to provide evidence for an association between a domain and either good mental health, poor mental health, or a negligible or inconsistent association. The overall quality of evidence across all studies for each domain was assessed using the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) guidelines. RESULTS: Forty-seven papers were eligible (mainland US 30 [64%], Canada 8 [17%], Australia 7 [15%], Hawaii 4 [9%]), including 58,218 participants aged 4-20 years. Most papers were cross-sectional (39, 83%) and measured negative mental health outcomes (41, 87%). Children's negative cohesion with their families and the presence of adverse events appeared the most reliable predictors of increased negative mental health outcomes. Children's substance use, experiences of discrimination, comorbid internalising symptoms, and negative parental behaviour also provided evidence of associations with negative mental health outcomes. Positive family and peer relationships, high self-esteem and optimism were associated with increased positive mental health outcomes. CONCLUSIONS: Quantitative research investigating Indigenous children's mental health is largely cross-sectional and focused upon negative outcomes. Indigenous children living in high income countries share many of the same risk and protective factors associated with mental health. The evidence linking children's familial environment, psychological traits, substance use and experiences of discrimination with mental health outcomes highlights key targets for more concerted efforts to develop initiatives to improve the mental health of Indigenous children.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.003 | 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".