Views on the usability and usefulness of the PeerConnect app among Ontario public safety professionals
Bibliographic record
Abstract
Public safety professionals (PSPs) (e.g. police officers, correctional workers, and paramedics) are regularly exposed to potentially psychologically traumatic events that leave them vulnerable to occupational and posttraumatic stress injuries. In response, PSP organizations have developed and implemented peer support programs, as well as peer support apps, to promote health awareness among workers. This study was designed to evaluate the usability, usefulness, efficacy (i.e. self-reported mental health), and thoughts moving forward on whether to continue providing the PeerConnect app. A sample of PSPs ( N = 455) from across 28 PSP organizations in Ontario, Canada, specifically 14 police services, 13 emergency services, and the Ontario provincial correctional service, participated in an online survey intended to explore PSPs’ views and experiences with the app. Of the 455 PSPs surveyed, 226 were PeerConnect users, and 229 were non-users. A series of t -tests and chi-square tests were conducted to compare users and non-users. Overall, the Connect feature was the most used by the PSPs. Among Connect users, 74.8% used the feature primarily to provide peer support, while 21.8% used the feature primarily to receive peer support. When assessing the usefulness of the app, 70.8% of app users were satisfied with the app, although only 40.3% of users found the app “extremely” or “very” useful in serving their well-being. When asked whether their organization should continue providing access to PeerConnect, 88.9% of users believed so. Findings demonstrate that PSPs who used PeerConnect were generally satisfied with the app and its features; however, there were barriers, such as a lack of internet access at work, that may have prevented non-users from using the app and highlighted the need for refinements in design, implementation, and integration into organizational culture. In response, we suggest public safety organizations consider finding innovative ways to continue to offer peer support opportunities for PSPs.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".