Measuring mental health in Indigenous young people: A review of the literature from 1998–2008
Bibliographic record
Abstract
BACKGROUND: Indigenous young people are disproportionately exposed to risk factors for poor mental health. Methodologically rigorous research will be critical in the development and evaluation of prevention and treatment programs. Research examining the mental health of Indigenous young people may have been undermined by poor measurement. The extent to which research has used measures with adequate psychometrics is unknown. METHODS: MEDLINE, PsychINFO and PUBMED databases, were systematically searched to identify papers published between 1998-2008 measuring the mental health of Indigenous young people from Australia, Canada, New Zealand or the US. Data extracted included type of mental health instrument, psychometric analyses reported and results. RESULTS: Fifty-four relevant studies were identified. Seventy-nine mental health instruments were used, and 18% were bespoke. Only 14% of instruments had been validated for the relevant Indigenous population. Few studies reported assessment of the reliability or validity of instruments. Data about both the reliability and validity of 10 measures were reported. None of the measures met the standards set by the review. Evidence of at least one type of reliability and validity was demonstrated for six measures. CONCLUSIONS: From 1998-2008 few studies of mental health in Indigenous young people used measurement instruments with previously determined reliability and validity.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".