Organizational Best Practices Supporting Mental Health in the Workplace
Bibliographic record
Abstract
OBJECTIVE: To provide a narrative review of best and promising practices for achieving exemplary mental health in the workplace as the foundation for the inaugural Carolyn C. Mattingly Award for Mental Health in the Workplace. METHODS: Research was drawn from peer-reviewed articles using the search terms associated with workplace mental health. RESULTS: Eight categories of best practices were identified: (1) culture, (2) robust mental health benefits, (3) mental health resources, (4) workplace policies and practices, (5) healthy work environment, (6) leadership support, (7) outcomes measurement, and (8) innovation. CONCLUSION: The review provided the scientific backing to support criteria developed for the Carolyn C. Mattingly Award for Mental Health in the Workplace. By recognizing organizations that apply evidence-based practices in their health and well-being programs, the Mattingly Award may inspire employers to adopt best practices.
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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 | 0.034 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".