Health impacts of the COVID-19 pandemic among Canadians living with disabilities
Bibliographic record
Abstract
The devastating impact of the COVID-19 pandemic on general health has received extensive attention in recent research. However, limited attention has been given to examining the health impacts of the pandemic among people living with disabilities. This study examines the impact of the COVID-19 pandemic on three aspects of health among Canadians living with a disability: (1) perceived physical health, (2) perceived mental health, and (3) unmet healthcare needs during the pandemic. We utilized crowdsourcing data from Statistics Canada’s Impacts of COVID-19 on Canadians Living with Long-term Conditions and Disabilities, 2020 Survey. The total sample size for our study was 8,872 and included males and females who were 15 years and older. To examine the health impacts of the pandemic, we calibrated a multivariable logistic regression. We found that respondents living with a disability had higher odds of experiencing negative impacts from the pandemic on their physical and mental health and have more unmet healthcare needs than those without a disability. Youth (15–24 years) living with a disability had 4.11 times higher odds of experiencing poor physical health during the pandemic than older adults (65 years and older) without a disability. Similarly, respondents aged 25-44 years and 45-64 years living with a disability also had higher odds of experiencing poor physical health (5.34 times and 5.68 times respectively) during the pandemic than older adults without a disability. The health impacts of the pandemic among those living with disabilities were found to differ significantly by age cohorts.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".