Understanding the Unmet Accommodation Needs of People Working with Mental or Cognitive Conditions: The Importance of Gender, Gendered Work, and Employment Factors
Bibliographic record
Abstract
PURPOSE: Workplace support needs for women and men living with mental health conditions are not well understood. This study examined workplace accommodation and support needs among women and men with and without mental health or cognitive conditions and individual and workplace factors associated with having unmet needs. METHODS: A cross-sectional survey of 3068 Canadian workers collected information on disability, gender, gendered occupations, job conditions, work contexts, and workplace accommodations. Multivariable logistic regression analyses examined gender- and disability-based differences in unmet needs for workplace flexibility, work modifications, and health benefits, and the association of work context (i.e., work schedule, job sector) and job conditions (i.e., precarious work) on the likelihood of unmet accommodation needs. The additive (i.e., super- or sub-additive) and multiplicative effects of disability, gender, and occupational gender distribution on the probability of unmet accommodation needs were also assessed. RESULTS: The most common unmet workplace accommodation was work modifications reported by 35.9% of respondents with mental/cognitive disability and workplace flexibility reported by 19.6% of individuals without a mental/cognitive disability. Women, employees in female dominant occupations, and participants with mental/cognitive disabilities were more likely to report unmet needs compared with men, employees in non-female dominant occupations, and participants without disabilities but these findings were largely explained by differences in job conditions and work contexts. No interacting effects on the likelihood of reporting unmet needs for workplace accommodations were observed. CONCLUSIONS: To support employee mental health, attention is needed to address work contexts and job conditions, especially for people working with mental/cognitive disabilities, women, and workers in female-dominated occupations where unmet accommodation needs are greatest.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".