Views on the Functionality and Use of the PeerConnect App Among Public Safety Personnel: Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Research supports that public safety personnel (PSP) are regularly exposed to potentially psychologically traumatic events and occupational stress, which can compromise their well-being. To help address PSP well-being and mental health, peer support is increasingly being adopted (and developed) in PSP organizations. Peer support apps have been developed to connect the peer and peer supporter anonymously and confidentially, but little is known about their effectiveness, utility, and uptake. OBJECTIVE: We designed this study to evaluate the functionality and use of the PeerConnect app, which is a vehicle for receiving and administering peer support. The app connects peers but also provides information (eg, mental health screening tools, newsfeed) to users; thus, we wanted to understand why PSP adopted or did not adopt the app and the app's perceived utility. Our intention was to determine if the app served the purpose of connectivity for PSP organizations implementing peer support. METHODS: A sample of PSP (N=23) participated in an interview about why they used or did not use the app. We first surveyed participants across PSP organizations in Ontario, Canada, and at the end of the survey invited participants to participate in a follow-up interview. Of the 23 PSP interviewed, 16 were PeerConnect users and 7 were nonusers. After transcribing all audio recordings of the interviews, we used an emergent theme approach to analyze themes within and across responses. RESULTS: PSP largely viewed PeerConnect positively, with the Connect feature being most popular (this feature facilitated peer support), followed by the Newsfeed and Resources. App users appreciated the convenience of the app and felt the app helped reduce the stigma around peer support use and pressure on peer supporters while raising awareness of wellness. PSP who did not use the app attributed their nonuse to disinterest or uncertainty about the need for a peer support app and the web-based nature of the app. To increase app adoption, participants recommended increased communication and promotion of the app by the services and continued efforts to combat mental health stigma. CONCLUSIONS: We provide contextual information about a peer support app's functionality and use. Our findings demonstrate that PSP are open to the use of mental health and peer support apps, but more education is required to reduce mental health stigma. Future research should continue to evaluate peer support apps for PSP to inform their design and ensure they are fulfilling their purpose.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".