How do supervisors perceive and manage employee mental health issues in their workplaces?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it