Bearing That in Mind: Canadian Teachers’ Experiences After Mental Health Literacy Training
Bibliographic record
Abstract
The rise in the number of mental health disorders in children and young people is a growing concern worldwide. As young people spend much of their time in a school environment, educational institutions and teachers are more frequently being asked to support students struggling with these afflictions. Programs intended to increase teachers’ mental health literacy (MHL) have tried to equip them for this task. Previous research related to MHL has largely focused on quantitative means of assessing teachers’ MHL in the short-term, with little qualitative focus on their long-term teaching experiences, or on the retention and application of these skills in teachers’ day-to-day practice. \n\tThis thesis explores the lived experiences of seven Canadian teachers from a variety of teaching environments, and at various stages of their careers, who have taken MHL training. It investigates how their understanding of this MHL training has informed their teaching through semi-structured phone interviews that were recorded, transcribed, and analyzed using Interpretative Phenomenological Analysis (IPA). Four themes emerged from the research, including the need for 1) contextually relevant training, 2) role acceptance, 3) the feasibility of applying knowledge, and 4) self-efficacy. \n \nThe findings of this study are intended to improve future MHL programming, implementation, and ultimately early and improved mental health outcomes for students while also outlining directions for future research.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".