Disability and sex/gender intersections in unmet workplace support needs: Findings from a large Canadian survey of workers
Bibliographic record
Abstract
INTRODUCTION: Individual attributes including disability and sex/gender have the potential to intersect and determine the likelihood of unmet workplace support needs. Our study compares unmet workplace support needs between workers with and without a disability, and according to disability type and sex/gender differences. METHODS: Workers with (n = 901) and without (n = 895) a disability were surveyed to examine their need and use of workplace supports including job accommodations, work modifications and health benefits. A multivariable logistic model was conducted to examine the relationship between disability status, disability type and sex/gender and unmet workplace support needs. The model included interaction terms between sex/gender × physical disability, sex/gender × nonphysical disability, and sex/gender × physical and nonphysical disability. RESULTS: Among participants with a disability, 24% had a physical disability, 20% had a nonphysical disability (e.g., cognitive, mental/emotional or sensory disability) and 56% had both physical and nonphysical disability. Over half of the respondents were women (56%). Results from the multivariable model showed that nondisabled women were more likely to report unmet workplace support needs when compared to nondisabled men (odds ratio [OR] = 1.54, 95% confidence interval [CI], 1.13-2.10). Findings also showed an intersection between the number and type of disability and sex/gender; women with both a physical and nonphysical disability had the greatest likelihood of reporting unmet workplace support needs when compared to nondisabled men (OR = 2.73; 95% CI, 1.83-4.08). CONCLUSIONS: Being a woman and having one or more disabilities can determine unmet workplace support needs. Strategies to address workplace support needs should consider the intersection between disability and sex/gender differences.
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How this classification was reachedexpand
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".