Using Mental Health First Aid Training to Improve the Mental Health Literacy of Physiotherapy Students
Bibliographic record
Abstract
Purpose: Mental Health First Aid (MHFA) training has been proven to improve the literacy of trainees and reduce the stigma they may have toward individuals with mental health problems in the general population. Our research was designed to determine whether MHFA training had an impact on physiotherapy students’ attitudes toward psychiatry and mental illness, their confidence to engage with people with mental health problems, and their preparedness for practice. Method: Final-year students from one university who had finished MHFA training completed a questionnaire that included the Attitudes Toward Psychiatry–30 and questions about their perceived confidence in working with people with mental illness and preparedness for practice. Their responses were compared with those from a previous cohort of students at the same point in their university education who had not completed MHFA training. Results: The students who had completed MHFA training (response rate 83%) had a more positive attitude toward psychiatry and mental illness than those who had not ( p < 0.001). Their confidence in treating people with mental health problems also increased, and to a statistically significant extent ( p < 0.001). Conclusions: MHFA training appeared to improve students’ attitudes toward psychiatry and mental health, increase their confidence in treating people with mental health problems, and better prepare them for practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".