Clarifying the concept of mental health literacy: Protocol for a scoping review
Bibliographic record
Abstract
This scoping review will map the peer-reviewed and grey literature, to clarify the concept of mental health literacy (MHL). MHL is an emerging area of study within mental health promotion, as programming and policy efforts devoted to promoting mental health emerge. Enhancing MHL in the general population is a strategy for promoting mental health by reducing stigma and empowering individuals to recognize, interpret, and understand their mental health, and know when to seek help for themselves and others. Despite the positive outcomes associated with MHL, conceptualization varies in scope, purpose, process, and outcome; there is little consensus of what “counts” as MHL. A clearly defined conceptualization of MHL is needed to support research, programing, and policy in mental health promotion. Papers on the theoretical and conceptual principles underlying MHL and primary studies documenting MHL initiatives, and methods, will be included. A scoping literature search will be performed following the search protocol for scoping reviews by the Joanna Briggs Institute (JBI) to identify all relevant literature on MHL. Searches will be conducted in three scientific databases; there will be no time limit imposed, although all sources must be written in English. Identifying the conceptualizations of MHL in the literature that is guiding mental health interventions will provide conceptual clarity ultimately advancing knowledge of mental health literacy.
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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 | 0.152 | 0.165 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.083 | 0.024 |
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".