Hearing disability and employment: a population-based analysis using the 2017 Canadian survey on disability
Bibliographic record
Abstract
Purpose: The objectives of this study were to determine the effects of hearing disability on employment rates; examine how various factors are associated with employment; and identify workplace accommodations available to persons with hearing disabilities in Canada.Material and methods: A population-based analysis was done using the data collected through the 2017 Canadian Survey on Disability (CSD), representing 6 million (n = 6 246 640) Canadians. A subset of the complete dataset was created focusing on individuals with a hearing disability (n = 1 334 520). Weighted descriptive and multivariate logistic regression analyses were performed.Results: In 2017, the employment rates for working-age adults with a hearing disability were 55%. Excellent general health status (OR: 3.37; 95% CI: 2.29–4.96) and daily use of the internet (OR: 2.70; 95% CI: 1.78–4.10) had the highest positive effect on the employment rates. The top three needed but least available accommodations were communication aids (16%), technical aids (19%), and accessible parking/elevator (21%).Conclusion: Employment rates for persons with a hearing disability are lower than the general population in Canada. Employment outcomes are closely associated with one’s general health and digital skills. Lack of certain workplace accommodations may disadvantage individuals with a hearing disability in their employment.Implications for RehabilitationPeople with severe hearing disabilities and those with additional disabilities may need additional and more rigorous services and supports to achieve competitive employment.It is important for the government to improve efforts toward inclusive education and develop strategies that promote digital literacy for job seekers with hearing disabilities.Officials concerned with implementing employment equity policies in Canada should focus on finding strategies that enable employees to have supportive conversations with their employers regarding disability disclosure and obtaining required accommodations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".