Adaptation, implementation, and evaluation of a protective mental health intervention (Resilient minds) for Canadian volunteer firefighters
Bibliographic record
Abstract
PURPOSE: The study was designed to assess the adaptation, implementation, and delivery of the Resilient Minds program for volunteer firefighters in Prince Edward Island. METHODS: A concurrent triangulation mixed-methods approach was used. Survey data were collected from firefighters who participated in the training, and semi-structured interviews were performed with peer trainers, members of leadership, and firefighters who participated in the implementation. Summary statistics were performed to summarize the survey data, and thematic analysis was performed to analyze the qualitative data following interpretive description methods. We followed the Consolidated Framework for Implementation Research to guide our data collection and analyses. RESULTS: The relative advantage of Resilient Minds (i.e., created for firefighters) and the high-priority need for mental health training promoted buy-in from stakeholders and facilitated program implementation. Most participants described the training as helpful (84 %), and reported high intention to use the information (86 %). At three-month follow-up, most reported being able to recall the training (83 %), and reported increased support from colleagues (59 %) and leadership (50 %). Our qualitative data identified improvements in coping and awareness of mental health issues, and decreases in mental health stigma. Suggestions were provided to improve the adaptation and implementation (e.g., simplifying the content, obtaining funding). CONCLUSION: Our results support the adaptation, implementation, and delivery of Resilient Minds among Canadian volunteer firefighters. Scaled delivery of the training across Canada will require collaborations among the developers, local implementation leads, and other stakeholders in the local fire services to adapt the course content and implementation procedures for the needs of each fire department.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".