Measurement properties of tools measuring mental health knowledge: a systematic review
Bibliographic record
Abstract
BACKGROUND: Mental health literacy has received great attention recently to improve mental health knowledge, decrease stigma and enhance help-seeking behaviors. We conducted a systematic review to critically appraise the qualities of studies evaluating the measurement properties of mental health knowledge tools and the quality of included measurement properties. METHODS: We searched PubMed, PsycINFO, EMBASE, CINAHL, the Cochrane Library, and ERIC for studies addressing psychometrics of mental health knowledge tools and published in English. We applied the COSMIN checklist to assess the methodological quality of each study as "excellent", "good", "fair", or "indeterminate". We ranked the level of evidence of the overall quality of each measurement property across studies as "strong", "moderate", "limited", "conflicting", or "unknown". RESULTS: We identified 16 mental health knowledge tools in 17 studies, addressing reliability, validity, responsiveness or measurement errors. The methodological quality of included studies ranged from "poor" to "excellent" including 6 studies addressing the content validity, internal consistency or structural validity demonstrating "excellent" quality. We found strong evidence of the content validity or internal consistency of 6 tools; moderate evidence of the internal consistency, the content validity or the reliability of 8 tools; and limited evidence of the reliability, the structural validity, the criterion validity, or the construct validity of 12 tools. CONCLUSIONS: Both the methodological qualities of included studies and the overall evidence of measurement properties are mixed. Based on the current evidence, we recommend that researchers consider using tools with measurement properties of strong or moderate evidence that also reached the threshold for positive ratings according to COSMIN checklist.
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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 | 0.075 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".