Supporting Police Well-Being Through an Adaptive Shift Management System: Co-Design Study
Bibliographic record
Abstract
BACKGROUND: Police personnel work under challenging conditions commonly associated with complex shift patterns, unpredictable last-minute changes, and high stress levels, with shift work identified as the major contributor to police personnel health and well-being challenges. These challenges negatively impact their mental well-being, physical health, and job performance, leading to potential health concerns such as fatigue, poor sleep, long-term physical disabilities, anxiety, and poor work-life balance. Existing digital interventions fail to address the needs of shift workers due to focusing solely on conventional 9-to-5 schedules. This gap highlights the need for tailored interventions that incorporate shift management systems into health and well-being applications to support emergency service personnel. OBJECTIVE: This study aimed to co-design a shift management system that can be incorporated into a well-being app tailored to address the health and well-being challenges caused by shift work in police personnel. METHODS: This study used interactive management methodology combined with user-centered design and cocreation to facilitate 6 co-design workshops with a diverse group of stakeholders. Each session was structured around idea generation, structural prioritization, and iterative prototyping. User-centered design principles such as persona mapping, scenario walk-throughs, and structured feedback exercises were integrated into the workshop sessions to ensure that the system met diverse user needs. Data were analyzed through participatory feedback and thematic analysis, which allowed for continuous iteration and prioritization of system features based on stakeholder inputs. RESULTS: Participants highlighted the need for a shift management system capable of managing complex and variable shift schedules with real-time adaptability and support for work-life balance. Thematic analysis revealed shift management challenges such as limited flexibility in accommodating schedule changes and issues managing rotating shift patterns. In response to these identified challenges, a prototype was developed that included features such as bulk creation and modification of shift schedules, shift customization, and visualization tools for monitoring and identifying shift trends and reusable patterns for efficiency. This study demonstrated that integrating a schedule management system into a well-being app could provide personalized support based on users' shift schedules. The integration showed significant potential in supporting the health and well-being of police personnel. CONCLUSIONS: The co-designed shift management system demonstrated strong feasibility and high acceptability among the participants. The integration of shift scheduling into a well-being app can provide tailored support across several domains such as nutrition, hydration, sleep, and physical activity. This combination shows promise in providing a sustainable approach to enhancing the health, well-being, and work-life balance of personnel working in high-stress occupations such as policing.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".