PeerOnCall: Exploring how organizational culture shapes implementation of a peer support app for public safety personnel
Bibliographic record
Abstract
BACKGROUND: Public safety personnel (PSP) such as firefighters, paramedics, and police are exposed to traumatic situations, which increase their risk for mental health issues. However, many PSP do not seek help in a timely manner. Peer support interventions have the potential to decrease stigma and increase treatment-seeking behaviours among PSP. However, little is known regarding how the organizational culture of public safety organizations (PSOs) affects the implementation of a peer-based intervention. This study aims to understand the extent to which organizational culture, including masculinity contest cultures (MCC), within Canadian PSOs could affect implementation of PeerOnCall, a new peer support app for PSP. METHODS: A qualitative multiple case study design was adopted, integrating semi-structured interviews with organizational champions from five PSOs. One to three champions from each PSO acted as key informants regarding their organizations. Interviews explored champions' perceptions of how organizational culture might shape implementation. Interview data were analyzed using inductive thematic analysis. RESULTS: Three themes were identified in analysis of the champion interviews. The first theme focused on external drivers and the second theme focused on internal drivers of organizational culture shift. The third theme focused on how culture can create resistance to implementation. Importantly, the MCC norm of show no weakness was described as a source of potential resistance when implementing the app. CONCLUSIONS: Each PSO had a unique and changing culture. Understanding how champions anticipate the role of culture in shaping implementation of an app-based intervention like PeerOnCall can guide the creation of contextually relevant strategies that optimize implementation within PSOs. Recommendations for optimizing implementation and areas for further study are provided.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".