Employers’ Perspectives on Hiring and Accommodating Workers With Mental Illness
Bibliographic record
Abstract
Many individuals with mental illness want to return to work and stay in employment. Yet, there is little research that has examined the perspectives of employers on hiring and accommodating these workers and the kinds of supports employers need to facilitate their reintegration into the workforce. The aim of the current research was to explore the challenges employers face and the support they need to hire and accommodate workers with mental illness (WWMI). A qualitative research design guided by a grounded theory approach was used. In-depth interviews were conducted with 28 employers selected from a wide range of industries in and around Edmonton, Canada. The employers were a mix of frontline managers, disability consultants, and human resource managers who had direct experience with hiring and supervising WWMI. Data were analyzed using the principles of grounded theory. The findings highlight several challenges that employers face when dealing with mental health issues of workers in the workplace. These challenges can act as barriers to hiring and accommodating WWMI.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".