Disability—a chronic omission in health equity that must be central to Canada’s post-pandemic recovery
Bibliographic record
Abstract
Highlights • People with disabilities in Canada have experienced excess risk of COVID-19 infections and mortality but have not received adequate policy support throughout the pandemic.• Canada's post-pandemic recovery for health care and public health must involve and include Canadians with disabilities.• Any post-pandemic recovery should improve the accessibility of health care, address key social determinants of health for Canadians with disabilities (with an emphasis on housing and employment), increase representation of people with disabilities in health care and public health, and focus on disability considerations in future pandemic preparedness.with disabilities in Canada's universal health system.8 Strengthening of health systems post-pandemic must focus on the rights of people with disabilities in order to correct these long-standing inequities.Many of the lessons from the COVID-19 pandemic can help achieve this goal.For instance, messaging on care rationing and mortality risk revealed deeply rooted and problematic assumptions on the value of disabled lives globally 9 -assumptions that Keywords: disability, COVID-19, pandemic recovery, health equity Expand and improve the accessibility of health care services for Canadians with disabilities Several barriers exist for quality, rapid, equitable and affordable care for people
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".