Even if you build it, they may not come: challenges in the uptake of workplace mental health toolkits
Bibliographic record
Abstract
BACKGROUND: Strategies to promote workplace mental health can target system, organization, team, and individual levels exclusively or in concert with each other. Creating toolkits that include these different levels is an emerging innovative strategy to support employees working in various sectors. Our paper describes the development, implementation, and refinement of two different online toolkits: the Healthy Professional Worker Toolkit for Education Workers and the Health Worker Burnout Toolkit. METHODS: The Knowledge to Action Framework guided the team during the development and early interventions phases of toolkit development. Stakeholder engagement regarding the intended use of the toolkit of promising practices for workplace interventions was integrated throughout with different forms of feedback in a research capacity between 2022 and 2024. RESULTS: Reflecting on the different phases of the KTA Framework, we describe first the engagement involved in building the toolkits and then on their utilization. Our toolkits were built to include different resources aimed at empowering workers, teams, and employers offering innovative ideas to address the mental health-leaves of absence and return to work cycle in one case and the different forms and consequences of burnout in the other. Criteria for inclusion were informed by ongoing research with a range of stakeholders and other intended toolkit users including managers, supervisors, executives, human resource specialists, staff, and others in healthcare and educational organizations and settings. In the implementation phase, the volume of resources available in each toolkit considered a strength by some was overwhelming for some partners and individual workers to navigate. Capacity, engagement, time, and readiness for change, are themes that heavily influenced if and when organizations interacted with each toolkit, and how much time they spent exploring the resources provided. CONCLUSION: It is critical to ground toolkits in the experiential evidence of workplace mental health as is linking these to evidence-informed interventions that correspond to workplace concerns. Organizational readiness to adopt and adapt resources and implement changes is a key consideration. Ultimately, user engagement is what brought these toolkits to life.
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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 | 0.207 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.008 | 0.031 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".