Disability and unmet need for health care in Canada: Findings from the Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: People with disabilities may experience disparities in access, quality, and outcomes of care compared to those without disabilities. However, there is limited Canadian evidence on the level of unmet need for care in this population. OBJECTIVE: This study investigated the level of unmet health needs in the Canadian population, with and without disabilities, as well as the factors associated with unmet needs, using a nationally representative survey. METHODS: This cross-sectional study used data from the Canadian Community Health Survey (n = 60,995) to examine self-reported unmet need for health care in the last 12 months. Multivariable logistic regression models were used to evaluate the association between unmet needs, disability status, sociodemographic characteristics, and having a regular primary care provider. RESULTS: Individuals with a disability were over four times more likely to report unmet health care needs than those without a disability, after adjusting for sociodemographic factors. The most common reason for unmet needs was poor availability of care, including long wait times and gaps in regional care. Having a regular care provider significantly reduced the likelihood of having unmet health care needs; however, disability status remained an independent determinant of unmet need. CONCLUSIONS: The findings highlight the need for targeted policy initiatives to reduce health care access disparities among individuals with disabilities. Improving the availability and timely provision of care that is responsive to the specific needs of this population may help address unmet needs.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".