PeerOnCall: Evaluating Implementation of App-Based Peer Support in Canadian Public Safety Organizations
Bibliographic record
Abstract
Public safety personnel (PSP), including correctional workers, firefighters, paramedics, police, and public safety communicators, are at increased risk for posttraumatic stress injury, yet face barriers in receiving timely support. Mobile health (mHealth) applications (apps) offer promising avenues for confidential, on-demand access to relevant information and support. The purpose of this study was to assess implementation of PeerOnCall, a new mHealth platform designed by and for PSP (the platform includes two parallel apps: one for frontline workers and one for peer support providers). A multi-site mixed methods implementation trial was conducted over 3−6 months in 42 public safety organizations across Canada. App usage trends were tracked through software analytics, and facilitators and barriers to app use were explored via interviews with organizational champions. Over 11,300 employees across 42 organizations were invited to use the PeerOnCall app over the trial period, with approximately 1759 PSP (15% of total) downloading the app. Variation within and across sectors was evident in app downloads and feature use. Approaches to communication (mode, timing, and messenger), and organizational culture related to mental health and help outreach affected uptake levels. PeerOnCall is a promising tool to facilitate access to peer support; however, culturally relevant strategies are needed to overcome barriers and integrate this tool into workplace practices.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".