Assessing service use for mental health by Indigenous populations in Australia, Canada, New Zealand and the United States of America: a rapid review of population surveys
Bibliographic record
Abstract
Abstract Background Indigenous people in Australia, Canada, New Zealand and the United States of America experience disproportionately poor mental health compared to their non-Indigenous counterparts. To optimally allocate resources, health planners require information about the services Indigenous people use for mental health, their unmet treatment needs and the barriers to care. We reviewed population surveys of Indigenous people to determine whether the information needed to guide service development is being collected. Methods We sought national- or state-level epidemiological surveys of Indigenous populations conducted in each of the four selected countries since 1990 that asked about service use for mental health. Surveys were identified from literature reviews and web searches. We developed a framework for categorising the content of each survey. Using this framework, we compared the service use content of the surveys of Indigenous people to each other and to general population mental health surveys. We focused on identifying gaps in information coverage and topics that may require Indigenous-specific questions or response options. Results Nine surveys met our inclusion criteria. More than half of these included questions about health professionals consulted, barriers to care, perceived need for care, medications taken, number, duration, location and payment of health professional visits or use of support services or self-management. Less than half included questions about interventions received, hospital admissions or treatment dropout. Indigenous-specific content was most common in questions regarding use of support services or self-management, types of health professionals consulted, barriers to care and interventions received. Conclusions Epidemiological surveys measuring service use for mental health among Indigenous populations have been less comprehensive and less standardised than surveys of the general population, despite having assessed similar content. To better understand the gaps in mental health service systems for Indigenous people, systematically-collected subjective and objective indicators of the quality of care being delivered are needed.
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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 | 0.048 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.033 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".