Developing families-forward innovations in public safety personnel organizations
Bibliographic record
Abstract
Public safety personnel (PSP) experience stressors which may affect their mental health, occupational responsibilities, performance on duty, and their families. In the current article, we explore the development and implementation of family-forward initiatives within public safety organizations. In reflecting on public safety implementation leaders' experiences of developing and implementing family-forward innovations, we aim to understand the crucial constructs involved in developing and implementing effective innovations. Fifteen public safety leaders with previous experience implementing a family-forward initiative, from four public safety sectors, participated in semi-structured interviews about their experiences with implementing family-forward initiatives to reveal the conditions under which these initiatives were conducted among different public safety contexts. We mapped the interview data onto the Consolidated Framework of Implementation Research (CFIR), and present the relevant constructs needed to be considered for future initiatives. Participants identified four types of family-forward innovations implemented across four public safety sectors: i) community building, ii) social norming, iii) building awareness and skills, and iv) communication channels. Leadership, resources, and a climate of readiness were instrumental in the implementation of family-forward initiatives. Knowledge of these implementation constructs can be used to improve and adapt family-forward initiatives for the future as well as for development and implementation of initiatives for other public safety sectors. • Four types of family-forward initiatives: community building, social norming, building awareness and skills, and communication channels. • Utilizing the Consolidated Framework for Implementation Research, we were able to present the relevant constructs needed to be considered for future initiatives. • Major barriers and facilitators occurred around resources, readiness, and leadership.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".