The quality of mental health literacy measurement tools evaluating the stigma of mental illness: a systematic review
Bibliographic record
Abstract
AIMS: Stigma of mental illness is a significant barrier to receiving mental health care. However, measurement tools evaluating stigma of mental illness have not been systematically assessed for their quality. We conducted a systematic review to critically appraise the methodological quality of studies assessing psychometrics of stigma measurement tools and determined the level of evidence of overall quality of psychometric properties of included tools. METHODS: We searched PubMed, PsycINFO, EMBASE, CINAHL, the Cochrane Library and ERIC databases for eligible studies. We conducted risk-of-bias analysis with the Consensus-based Standards for the Selection of Health Measurement Instruments checklist, rating studies as excellent, good, fair or poor. We further rated the level of evidence of the overall quality of psychometric properties, combining the study quality and quality of each psychometric property, as: strong, moderate, limited, conflicting or unknown. RESULTS: We identified 117 studies evaluating psychometric properties of 101 tools. The quality of specific studies varied, with ratings of: excellent (n = 5); good (mostly on internal consistency (n = 67)); fair (mostly on structural validity, n = 89 and construct validity, n = 85); and poor (mostly on internal consistency, n = 36). The overall quality of psychometric properties also varied from: strong (mostly content validity, n = 3), moderate (mostly internal consistency, n = 55), limited (mostly structural validity, n = 55 and construct validity, n = 46), conflicting (mostly test-retest reliability, n = 9) and unknown (mostly internal consistency, n = 36). CONCLUSIONS: We identified 12 tools demonstrating limited evidence or above for (+, ++, +++) all their properties, 69 tools reaching these levels of evidence for some of their properties, and 20 tools that did not meet the minimum level of evidence for all of their properties. We note that further research on stigma tool development is needed to ensure appropriate application.
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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 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".