Adoption of app-based peer support by Canadian healthcare providers: A mixed-methods organizational implementation study (Preprint)
Bibliographic record
Abstract
Background: There is an urgent and critical need to support the mental health of health care providers, given high rates of stress and burnout. Although the issues are complex, digital access to information and support can help address the needs, as technology can facilitate on-demand links to private, customized resources, including peer support. Beyond Silence (McMaster University) is an evidence-informed mobile health platform co-designed with health care workers and grounded in prior evidence that mental health literacy and peer support can reduce stigma and facilitate earlier help-seeking. Objective: This study aimed to (1) explore how health care workers across diverse health care settings use the app and (2) identify opportunities and barriers to implementation. Methods: A multiple-case study framework, informed by the Consolidated Framework for Implementation Research (CFIR), was applied to capture 4 months of implementation across a purposive sample of 7 diverse Canadian health care organizations. Implementation within each organization was led by designated organizational champions who leveraged existing communication channels and standardized promotional materials to invite employees to voluntarily download and use the app. Implementation outcomes were assessed using app analytics (downloads and feature use) and semistructured baseline and follow-up interviews with organizational champions to explore contextual influences on uptake. Results: Approximately 1066 employees downloaded the app over the 4-month period, ranging from <2% to >45% of employees across the 7 organizations. Interviews with 28 organizational champions noted that there was good leadership support for the technology, aligning with their mission to address employee mental health. Barriers to use, however, included workplace culture surrounding mental health and help-seeking, lack of awareness about when and how to use the app, and infrastructure-related challenges, such as limited time and a lack of private spaces to download and use the technology. Conclusions: Effective implementation is a precondition for positive outcomes; therefore, strategies are needed to optimize technology implementation. Recommendations include evaluating organizational readiness, building mental health literacy, creating a multimodal communication and implementation plan, addressing technology requirements, and embedding the technology into organizational policies and practices. This study highlights key challenges in the implementation of the Beyond Silence peer support platform for health care workers, including slow adoption linked to mental health stigma, competing demands, and limited frontline engagement. Addressing these barriers will require innovative, trust-building strategies to support meaningful uptake and sustained use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".