Sharing knowledge on implementing mental health and wellbeing projects for veterans and first responders
Bibliographic record
Abstract
PURPOSE: The aim of this study was to describe the knowledge to action and knowledge transfer approaches used in an international mental health research funding program and its outcomes. A key goal of the evaluation was to maximise organisational learning and knowledge sharing to inform future implementation projects. METHODS: A series of interactive knowledge sharing workshops focused on five key themes: peer support; psychoeducation; the involvement of family, friends, and significant others; retreat, residential, or group-based programs; and organisational change. Qualitative descriptive analysis was used to code, summarise and describe themes. FINDINGS: Key learnings that influenced the success of mental health initiatives include building relationships across all organisational levels, involving Veterans and First Responders with lived experience in the design and implementation process, and understanding the unique workplace culture and operations. CONCLUSION: Our findings highlight the need for collaborative, informed approaches tailored to the culture, organisation and mental health support needs of Veteran and First Responder. These insights enhance understanding of the factors that impact the successful implementation of mental health prevention and support programs for those exposed to work-related trauma.
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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 | 0.060 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".