Examining Pre-service Teachers Mental Health Literacy Knowledge and Their Experiences of Mental Health Concerns of Students in PEI Schools
Bibliographic record
Abstract
Mental health literacy is fundamental to improving knowledge about mental health, decreasing stigma, and, therefore, enhancing help-seeking behaviors. This mixed method study (pre, post, and follow-up) evaluates a group of UPEI’s Faculty of Education English pre-service teachers’ regarding their knowledge, attitudes, and help-seeking efficacy, before, immediately after nine hours of training based on a mental health literacy curriculum resource, the Guide Professional Development Program (GPPDP), as well as at 3-month follow-up. Qualitative data will be collected from 20 students, selected randomly for two focus groups (10 Middle/High school and 10 Elementary school cohort) following pre-service teachers’ eight-week practicum placement in Prince Edward Island schools. Focus group interviews will be digitally recorded, transcribed and analyzed regarding pre-service teachers experiences around student mental health concerns in schools and the impact their training in mental health literacy has on these experiences. It is anticipated that pre service teachers will show an increase in mental health knowledge, a decrease in stigma, and these outcomes would be maintained at 3 month follow up. Additionally, it is expected pre service teachers will report increased help-seeking intentions with students regarding mental health concerns and positive outcomes as a result of having taken MHL training.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".