Bibliographic record
Abstract
This article presents results from an exploratory study of what college students from northern Ontario think about and do to manage their mental health. Data gathered in semistructured interviews were analyzed using the constant comparative method. The purpose of the study is to advance our understanding of the ontogeny, substantive nature and deployment of mental health literacy (MHL). MHL has hitherto been defined as “knowledge and beliefs about mental disorders that aid their recognition, management or prevention.”. This definition effectively translates to knowledge of the contents of the Diagnostic and Statistical Manual of Mental Disorders, currently in its fifth edition. Results of the study suggest that the current definition of MHL is overly narrow, that individuals use knowledge of various types from various sources to manage their mental health, and that the literacies that inform mental health management practices are developed through iterative engagement in autologous knowledge-translation, at the core of which are cultured resonance, meaning-making, metacognitive evaluation, and heuristic experimentation. MHL is redefined as the self-generated and acquired knowledge with which people negotiate their mental health. Broadening the definition of MHL has potential to enhance the capacity of individuals and communities to manage mental health effectively.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".