Supporting Police Well-Being Through an Adaptive Shift Management System: Co-Design Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».