Patients' Perspectives of Accessibility and Digital Delivery of Factual Content Provided by Official Medical and Surgical Specialty Society Websites: A Qualitative Assessment
Bibliographic record
Abstract
BACKGROUND: Health care websites provide a valuable resource of health information to online consumers, especially patients. Official surgical and medical society websites should be a reliable first point of contact. OBJECTIVE: The primary aim of this study was to quantitatively assess medical and surgical society websites for content and highlight the essential features required for a high-quality, user-friendly society website. METHODS: Twenty specialty association websites from each of the regions, Australia, UK, Canada, Europe, and the USA were selected for a total of 100 websites. Medical and surgical specialities were consistent across each region. Each website was systematically and critically analysed for content and usability. RESULTS: The average points scored per website was 3.2 out of 10. Of the total (N=100) websites, 12 scored at least 7 out of 10 points and 2 scored 9 out of 10. As well, 35% (35.0/100) of the websites had an information tab for patients on their respective homepages while 38% (38.0/100) had download access to patient information. A minority of the websites included different forms of multimedia such as pictures and diagrams (24.0/100, 24%) and videos (18.0/100, 18%). CONCLUSIONS: We found that most society websites did not meet an adequate standard for delivery of information. Half of the websites were not patient accessible, with the primary focus being for health professionals. As well, most required logins for information access. Specialty health care societies should create patient-friendly websites that would be beneficial to all online consumers.
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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".