Psychiatric Morbidity in Indigenous Peoples From Australia and the Americas: Unexpected Findings From a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Indigenous populations are often considered at high risk of psychiatric disorder, but many studies do not include direct comparisons with the general population. Objectives: To undertake a meta-analysis of studies comparing the prevalence rates of psychiatric disorders in Indigenous populations in the Americas and Australia with those of other groups. Method: A systematic search of MEDLINE, PsycInfo, EMBASE and article bibliographies. We included empirical quantitative comparisons of the 12-month or lifetime prevalence of any psychiatric disorder in Indigenous and non-Indigenous populations. Findings: We found 17 studies (n = 72,419) from Australia, Latin America, Canada and the United States. Indigenous people were at greater risk of concurrent posttraumatic stress disorder (odds ratio [OR] = 1.62; 95% confidence interval [CI] = 1.20, 2.1), alcohol use disorders (OR = 1.93; 95% CI = 1.50, 2.48) and substance use (OR = 2.02; 95% CI = 1.40, 2.93). However, there were no differences between Indigenous and non-Indigenous groups in the prevalence of a range of depressive and anxiety disorders. In some studies, Indigenous rates were lower. The results did not vary greatly by continent or setting (e.g. urban/rural). There were no differences in the lifetime prevalence rates of any disorders. Conclusions: The reasons for these results are unclear. One explanation might be that assessment tools may not accurately measure psychiatric symptoms in Indigenous populations. Future research therefore must ensure that diagnostic instruments are validated for use with Indigenous people. Another possibility might be that risk factors for psychiatric illness are a complex interaction of educational, economic and socio-cultural factors that may vary from disorder to disorder. Interventions need to take into account that disadvantage is rarely due to one factor.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.026 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".