Exploring Mental Health Literacy in Canada: A Mixed-Method Cross-Sectional Study
Bibliographic record
Abstract
Introduction: Mental health literacy (i.e., mental health-related knowledge, attitudes, and behaviour; MHL) may be a key to reducing the burden of mental illness on the health system and to improving the overall population’s mental health through facilitating upstream mental health promotion. Objectives: The purpose of this mixed-method study is to explore the correlates of MHL in Atlantic Canada and assess the ability of residents to correctly diagnose a disorder, identify potential causes, and propose suitable treatments based on the medical model or a social prescribing model. Methods: A sample of Atlantic Canadians (N = 254) participated in this cross-sectional study, which included vignettes and measures of overall MHL, level of contact with people living with mental illness, and preferred level of social distance from people with mental illness. Results: We found that (a) social connections were more commonly prescribed for generalized anxiety relative to the medical model treatment recommendations, (b) panic disorder was least likely to be correctly identified, (c) general anxiety was disproportionately thought to be caused by external factors, (d) only social distance predicts MHL beyond demographics and level of contact, and (e) household (not individual) conservative orientation negatively predicts MHL. Conclusion: Efforts to improve MHL and thus reduce the burden of mental illness on Atlantic Canadian health systems could be informed by increasing public knowledge of the causes and treatments of generalized anxiety disorder, increasing residents’ ability to recognize disorders beyond depression (e.g., panic disorder), and reducing stigma by fostering comfort for those living near individuals with mental illness.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".