Co-designing a family-forward initiatives toolkit for public safety organizations
Bibliographic record
Abstract
Public safety personnel (PSP) and their families are significantly affected by occupational stressors, including exposure to potentially psychologically traumatic events, physical risks, and operational demands that disrupt family dynamics. While PSP frequently rely on family support, these families often lack formal resources, contributing to work-family conflict and diminished well-being. This study employed a co-design methodology to develop a family-forward toolkit for Canadian public safety organisations, aiming to support the development and implementation of family-forward initiatives. Drawing on previous findings from a qualitative systematic review, environmental scan, and interviews, the toolkit was collaboratively co-designed through iterative focus groups, interviews, and a final survey. PSP participants validated the toolkit’s relevance and usability, highlighting its applicability across organisational roles and family readiness levels. The toolkit provides tailored initiative recommendations, planning worksheets, and strategic prompts aligned with organisational context and readiness. Feedback emphasised its practicality, with areas for refinement including user interface design, clarity of terminology, and inclusion of implementation supports. This study demonstrates the value of participatory design in developing evidence-informed, context-sensitive resources for high-risk occupational groups. Future research should include PSP family members in toolkit refinement and assess real-world implementation outcomes.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".