Mental Health Training Programs for Managers: What do Managers Find Valuable?
Bibliographic record
Abstract
Effective management of mental illness in the workplace has been identified as critical to decreasing its impact and developing a healthy workplace. Educational programs targeting managers have been held up as one way of developing effective management practices. While there are recommendations for what managers should do and how they should do it, there is little literature reflecting the managers' voices and what they value. For example, what skills would they like to learn related to mental illness and the workplace? What questions do they have about mental illness? What is their preference for how the material is delivered? Without answers to questions such as these, it is difficult to develop effective training programs for this key group. This paper seeks to add to the body of knowledge about designing mental health training programs for managers. We analyze responses of managers who attended workshops designed to teach them skills to address workplace mental health problems. The paper's three main objectives are to identify (a) aspects of the workshop most valued by participants, (b) areas of information and support managers consider helpful, and (c) barriers in the workplace that make managing mental illness challenging.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".