Scoping the Need for a Tailored mHealth App to Improve Health and Well-being Behavioral Transformation in the Police: Exploring the Views of UK Police Workers via Web-Based Surveys and Client Meetings
Notice bibliographique
Résumé
BACKGROUND: Police officers often work long, unsocial hours in a highly pressurized environment and may experience difficulties in managing their health and well-being. Their jobs can be highly stressful and feature unusual working hours and multiple shift patterns. When considering the policing environment of 2021, many roles that were previously the domain of warranted officers are now being carried out by nonwarranted police staff equivalents. These police staff roles are relatively new to policing but put staff under some of the same stresses as police officers. A UK police force requested help to investigate technologies that could be used to improve health and well-being and research how these technologies could be used to measure and track health behavior change. OBJECTIVE: Historical research studies need to be appraised in light of this new policing environment, and new research also needs to include this shift in dynamics when considering aspects of policing, including their health and well-being. This study explores police officer and staff attitudes toward and their use of existing health-related technology, highlights existing practices, and gathers views about how technology could be used more effectively. METHODS: A web-based survey was completed by police officers and staff (N=213) during the initial period of the UK lockdown in 2020. The survey was designed to find the solutions that participants used outside of those supplied by their employer, identify issues or problems, and find what they would like a hypothetical app to focus on. Additional requirements data were captured through client meetings, including discussions concerning previously attempted solutions and those currently in place. Thematic analysis was undertaken to identify the key themes. RESULTS: Attitudes toward and uses of existing health-related technology were captured, and existing practices were highlighted. Participants identified a need for an app to consider that a user was on shift-an important point, as many issues and problems with elements of their health and well-being involved shift work. Data also highlighted that a multifunctional tool would be more beneficial to participants than focusing on just 1 element. The key features and four domains were identified for app coverage. The prioritized order of importance of the four domains was activity, food and diet, sleep, and fluid intake. CONCLUSIONS: For police officers and staff, research data suggest that there is a previously unidentified requirement for a mobile app that could provide an easily accessible platform for them to use, regardless of the current location; one that could provide guidelines on diet, lifestyle habits, and health behavior to help the user make informed decisions to assist in personalized behavior change. Notably, one which is multifunctional and which also aligns effectively with the irregular shift patterns of its users.
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 | 0,018 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».