How do supervisors perceive and manage employee mental health issues in their workplaces?
Bibliographic record
Abstract
BACKGROUND: Organizations have become increasingly concerned about mental health issues in the workplace as the economic and social costs of the problem continue to grow. Addressing employees' mental health problems and the stigma that accompanies them often falls to supervisors, key people in influencing employment pathways and the social climate of the workplace. OBJECTIVE: This study examines how supervisors experience and perceive mental illness and stigma in their workplaces. It was conducted under the mandate of the Mental Health Commission of Canada's Opening Minds initiative. METHODS: The study was informed by a theoretical framework of stigma in the workplace and employed a qualitative approach. Eleven supervisors were interviewed and data were analyzed for major themes using established procedures for conventional content analysis. RESULTS: Themes relate to: perceptions of the supervisory role relative to managing mental health problems at the workplace; supervisors' perceptions of mental health issues at the workplace; and supervisors' experiences of managing mental health issues at work. The research reveals the tensions supervisors experience as they carry out responsibilities that are meant to benefit both the individual and workplace, and protect their own well-being as well. CONCLUSION: This study emphasizes the salience of stigma and mental health issues for the supervisor's role and illustrates the ways in which these issues intersect with the work of supervisors. It points to the need for future research and training in areas such as balancing privacy and supports, tailoring disclosure processes to suit individuals and workplaces, and managing self-care in the workplace.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".