Mental health literacy measures evaluating knowledge, attitudes and help-seeking: a scoping review
Bibliographic record
Abstract
BACKGROUND: Mental health literacy has received increasing attention as a useful strategy to promote early identification of mental disorders, reduce stigma and enhance help-seeking behaviors. However, despite the abundance of research on mental health literacy interventions, there is the absence of evaluations of current available mental health literacy measures and related psychometrics. We conducted a scoping review to bridge the gap. METHODS: We searched PubMed, PsycINFO, Embase, CINAHL, Cochrane Library, and ERIC for relevant studies. We only focused on quantitative studies and English publications, however, we didn't limit study participants, locations, or publication dates. We excluded non-English studies, and did not check the grey literature (non peer-reviewed publications or documents of any type) and therefore may have missed some eligible measures. RESULTS: We located 401 studies that include 69 knowledge measures (14 validated), 111 stigma measures (65 validated), and 35 help-seeking related measures (10 validated). Knowledge measures mainly investigated the ability of illness identification, and factual knowledge of mental disorders such as terminology, etiology, diagnosis, prognosis, and consequences. Stigma measures include those focused on stigma against mental illness or the mentally ill; self-stigma ; experienced stigma; and stigma against mental health treatment and help-seeking. Help-seeking measures included those of help-seeking attitudes, intentions to seek help, and actual help-seeking behaviors. CONCLUSIONS: Our review provides a compendium of available mental health literacy measures to facilitate applying existing measures or developing new measures. It also provides a solid database for future research on systematically assessing the quality of the included measures.
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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 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.037 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".