Accessibility of Canadian COVID-19 Testing Locations for People with Disabilities During the Third Wave of the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Background Canadians with disabilities make up nearly a quarter of the population yet face barriers in accessing information about COVID-19 testing accessibility across the country. Objective No known studies evaluate the online information about the accessibility of COVID-19 testing locations. This study aimed to understand the accessibility of COVID-19 testing sites in Canada based on online information in March 2021. Methods Key accessibility features were identified to evaluate COVID-19 testing websites information on accessibility and data were extracted from the website to simulate the user experience of booking a COVID-19 test. Results All provinces and territories provided minimal accessibility information on their COVID-19 testing websites, except for Ontario. Out of 170 testing locations in Ontario, few had information about accessibility, with only 8.2% listing at least 3 of the 5 key accessibility features measured on their websites. Conclusions This paper demonstrates that more than a year into the pandemic, there existed a clear lack of accessibility information for Canadian testing locations for people with disabilities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".