MétaCan
Menu
Back to cohort
Record W2043279228 · doi:10.1080/14639230500367746

Accessibility compliance rates of consumer-oriented Canadian health care Web sites

2005· article· en· W2043279228 on OpenAlexafffundabout
Laura O’Grady

Bibliographic record

VenueMedical Informatics and the Internet in Medicine · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsWeb accessibilityWorld Wide WebComputer scienceHealth careWeb Accessibility InitiativeListing (finance)Inclusion (mineral)SoftwareWeb standardsWeb pageInternet privacyMultimediaWeb developmentBusinessWeb application securityPsychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.068
GPT teacher head0.468
Teacher spread0.399 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueMedical Informatics and the Internet in MedicineSame topicHealth Literacy and Information AccessibilityFrench-language works237,207