Mental Health First Aid Training for Allied Health Clinical Educators: A Pre- and Post-Evaluation
Bibliographic record
Abstract
Purpose: Health profession students may experience mental health issues during training, and clinical educators report that they don’t feel confident in supporting students with these issues. This study explored whether a customized Mental Health First Aid (MHFA) training programme changed the knowledge, perceptions, intentions, and confidence of clinical educators in supporting students with mental health issues in the workplace. Method: Twenty-four allied health clinical educators from a tertiary health service attended a two-day customized MHFA course. The educators completed assessments before (n = 21) and after (n = 23) the course. Quantitative data was analyzed using independent t-tests. Qualitative data was thematically analyzed using content analysis. Results: Knowledge improved significantly (p = <0.001). The confidence to manage students with mental health issues increased significantly (p < 0.001). A significant change in perception was only found with respect to a character in a scenario being dangerous or unpredictable. Intentions to assist co-workers and students with mental health issues improved for all items but not necessarily significantly. Conclusions: This programme improved educators’ knowledge of mental health, perceptions of people with mental health issues, intentions of providing help, and confidence to support people with mental health issues.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".