Workplace Mental Health Training in Health Care: Key Ingredients of Implementation
Bibliographic record
Abstract
OBJECTIVES: Despite growing awareness of the importance of workplace mental health training and an increasing number of educational resources, there is a gap in knowledge regarding what shapes training effectiveness. The purpose of this study was to compare and describe the active ingredients of 2 workplace mental health education programs for health care workers. METHODS: Within the context of a randomized clinical trial, a multimethod process evaluation was conducted to explore key process elements shaping implementation outcomes: the innovation, service recipients, service providers, and the organizational context. Data collection included descriptive statistics regarding program participation, postprogram interviews with a purposive sample of 18 service recipients, 182 responses to open-ended questions on postgroup and follow-up surveys, and field journal reflections on the process of implementation. Data analysis was informed by an interpretive description approach, using a process evaluation framework to categorize responses from all data sources, followed by within and cross-case comparison of data from both programs. RESULTS: Five key forces shaped the implementation and perceived outcomes of both programs: a contact-based education approach, information tailored to the workplace context, varied stakeholder perspectives, sufficient time to integrate and apply learning, and organizational support. The Beyond Silence program provided more opportunity for contact-based education, health care-specific content, and in-depth discussion of diverse perspectives. CONCLUSIONS: To increase mental health literacy and reduce stigma, workplace training should be based on best practice principles of contact-based education, with contextually relevant examples and support from all levels of the organization.
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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 | 0.053 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".