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
Bibliographic record
Abstract
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.
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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 | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".