A cluster RCT to improve workplace mental health in a policing context: Findings of a mixed‐methods implementation evaluation
Bibliographic record
Abstract
BACKGROUND: We conducted a cluster randomized trial of a workplace mental health intervention in an Australian police department. The intervention was co-designed and co-implemented with the police department. Intervention elements included tailored mental health literacy training for all members of participating police stations, and a leadership development and coaching program for station leaders. This study presents the results of a mixed-methods implementation evaluation of the trial. METHODS: Descriptive quantitative analyses characterized the extent of participation in intervention activities, complemented by a qualitative descriptive analysis of transcripts of 60 semistructured interviews with 53 persons and research team field notes. RESULTS: Participation rates in the multicomponent leadership development activities were highly variable, ranging from <10% to approximately 60% across stations. Approximately 50% of leaders and <50% of troops completed the mental health literacy training component of the intervention. Barriers to implementation included rostering challenges, high staff turnover and changes, competing work commitments, staff shortages, limited internal personnel resources to deliver the mental health literacy training, organizational cynicism, confidentiality concerns, and limited communication about the intervention by station command or station champions. Facilitators of participation were also identified, including perceived need for and benefits of the intervention, engagement at various levels, the research team's ability to create buy-in and manage stakeholder relationships, and the use of external, credible leadership development coaches. CONCLUSIONS: Implementation fell far short of expectations. The identified barriers and facilitators should be considered in the design and implementation of similar workplace mental health interventions.
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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 | 0.043 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 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".