The Working Mind: A Meta-Analysis of a Workplace Mental Health and Stigma Reduction Program
Bibliographic record
Abstract
OBJECTIVES: This article describes a meta-analysis of The Working Mind, a program that was developed to address workplace mental health. The basic program addresses issues related to stigma in the workplace, the use of a mental health continuum model to evaluate signs and indicators of mental illness, and the development of coping skills. A manager version further addresses issues such as how to work with an employee who struggles with mental health issues, workplace accommodations, and overall management issues. METHODS: A total of 8 replications evaluated program effects on stigma, self-reported resilience, and coping abilities. RESULTS: The implementation of the program was associated with moderate reductions in stigma and increased self-reported resilience and coping abilities. These results were generally consistent across settings and showed nonsignificant differences when various potential moderators of the program were evaluated (e.g., employees versus managers, public versus private sector, gender, age). Qualitative comments collected at the end of the program suggested that many program participants found the program to be helpful and that the skills were being employed. CONCLUSIONS: Directions for future research, including the need for a randomized trial of The Working Mind, are discussed. Overall, the results suggest that the program is successful in its aims, but further inquiry is encouraged.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".