Building Mental Health Literacy within Ontario (Canada) Health and Physical Education.
Bibliographic record
Abstract
The primary research question which drove this analysis asked: What is Mental Health Literacy (MHL) and how is this accomplished in elementary schools in Ontario, given the new directives within the 2019 Health and Physical Education curricular document? Utilizing a qualitative research mode, the understanding of MHL was investigated through a summative latent content analysis of the current Ontario provincial government positions arising from the recent release of 2019, Ontario Health and Physical Education curricular document. The document of 320 pages is broad and somewhat deep instigating several secondary questions which asked: How should MHL be achieved in Health and Physical Education? What level of MHL awareness should Ontario Physical Educators and students reach for at the elementary level? What are the existing MHL guidelines and orientations impacting Ontario teachers? To what extent are Ontario teachers urged via the new curricula to focus and emphasize MHL in schools. MHL is a priority in this era of pandemic and critical to growth and development of teachers, students and the wider community accounting for well-being or lack of therein. Keywords: mental health literacy, health and physical education, curriculum, pedagogy
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".