Accessibility compliance rates of consumer-oriented Canadian health care Web sites
Bibliographic record
Abstract
Vast amounts of consumer-based health care information are widely available on the World Wide Web. However, for some this material is inaccessible due to reliance on specialized computer equipment or software known as assistive technology. These tools, designed for people with sensory, physical, or learning disabilities, act as a median to interpret Web pages in accessible ways. Unfortunately, many websites, including those with health-related content are not designed to accommodate this equipment. No research has yet been published examining the extent of this problem in Canadian consumer-oriented health care sites. The purpose of this study was to investigate the percentage of accessible consumer-based health care websites of Canadian origin. A listing of such sites was randomly sampled for study inclusion. Each was assessed for accessibility based on the World Wide Web Consortium (W3C) Web Accessibility Initiative (WAI) Web Content Accessibility Guidelines (WCAG) 1.0 using the validation software Bobby. The results indicated that only about 40% of pages investigated were free of errors in accordance with WCAG 1.0 Priority 1 level. Websites should be constructed in compliance with these standards to better accommodate those using assistive devices.
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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".