The Effects of Mental Health First Aid Preparation on Nursing Student Self-Efficacy in Their Response to Mental Health Issues
Bibliographic record
Abstract
Background: Past studies show a high prevalence of nursing students experience depressive symptoms at varying levels of severity. Teaching nursing students early in their studies how to recognize these symptoms in themselves, their peers, or clients, and how to take appropriate action, may promote better outcomes. Studies in Australia and England have found that mental health first aid (MHFA) increases nursing students’ self-confidence when supporting those experiencing mental health crises. Limited Canadian studies regarding MHFA training exist. Purpose: To examine the effect of MHFA training on the self-efficacy of nursing students to deliver mental health first aid in a clinical setting and among peers. Methods: Participants for this study included 22 volunteer first- or second-year students from a 3-year accelerated Canadian Baccalaureate nursing program. Each volunteer answered three demographic questions and ranked their confidence level on a 100-point scale to perform five situation-specific MHFA actions for each of two scenarios (peer and clinical). Questionnaires were completed by participants before and after attending a 2-day, 14-hour training course on MHFA. Results: Paired t-tests performed on each questionnaire item revealed significant increases in confidence levels to perform situation-specific mental health first aid actions for each scenario from pre- to post-training. Cronbach’s alpha results show acceptable internal reliability for the five-item questionnaires (pre- and post-test for each scenario). Conclusion: Mental health first aid training appears to improve the self-efficacy of nursing students to deliver MHFA actions to clients and peers experiencing mental health crises.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".