Measurement and Design in Surveys of Teachers’ Mental Health Literacy: A Scoping Review
Bibliographic record
Abstract
Mental health literacy (MHL) was introduced four decades ago as a term referring to knowledge and beliefs about mental disorders that aid in their recognition, management, or prevention. This scoping review mapped the peer-reviewed literature to understand how MHL is defined, conceptualized, and measured in studies involving those becoming teachers (pre-service teachers) and working teachers (in-service teachers). The search was performed following the method for scoping reviews by the Joanna Briggs Institute (JBI). Searches were conducted in four scientific databases with no time limit, although all sources had to be written in English or French. Primary studies (N = 35) that measured MHL for pre- and in-service teachers provided a global snapshot of MHL conceptualization and measurement across five continents. Global conceptualizations of MHL were largely driven by the definition and measures developed by Jorm, though the definition by Kutcher et al. was used in one fourth of the papers. Few studies explicitly stated a theoretical framework. Most studies used closed-ended scales, or a combination of closed-ended scales and vignettes to measure MHL. From a closer examination of the results, Canada emerged as a major leader in teacher MHL. Future research in this area should aim to include vignette measures, especially for pre-service teachers, and explicit theoretical frameworks, including socio-ecological and social or structural determinants of health-related frameworks that take an intersectional approach to MHL.
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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 | 0.202 | 0.418 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".