Quality and readability of online health information on menopausal hormone therapy in Canada: what are our patients reading?
Bibliographic record
Abstract
OBJECTIVE: To assess the quality and readability of 24 of the most accessed menopause hormone therapy (MHT) websites by Canadian women. METHODS: The top 24 websites from Google, Bing, and Yahoo were identified using the search term "hormone replacement therapy." Five menopause specialists assessed website content quality using the DISCERN Instrument, Journal of the American Medical Association (JAMA) benchmarks, and Abbott's Scale. Two reviewers assessed website credibility using the Health on the Net Foundation Code of Conduct certification, and website readability using the Simple Measure of Gobbledygook, Flesch-Kincaid Grade Level, and Flesch-Kincaid Read Ease formulae. RESULTS: Scores for quality of information varied. The mean JAMA score was low at 2.3 ± 1.1 (out of 4). Only one website met all benchmarks. Fourteen websites (58%) had a good/excellent DISCERN score, while four (17%) had a poor/very poor score. For Abbott's Scale, both the mean authorship score at 2.2 ± 1.0 (out of 4) and mean content score at 45.9 ± 9.8 (out of 100) were low. Inter-rater reliability was high for all tools. Fifteen websites (63%) were Health on the Net Foundation Code of Conduct certified. The mean Flesch-Kincaid Read Ease was 42.7 ± 10.3, mean Flesch-Kincaid Grade Level was 12.3 ± 1.9, and mean Simple Measure of Gobbledygook grade level was 11.3 ± 1.5. Only one website presented content at a reading level recommended for the public. Websites meeting more JAMA benchmarks were significantly less readable (P < 0.05). CONCLUSION: Although good quality MHT information exists online, several resources are inaccurate or incomplete. Overall, these resources are not considered comprehensible by the public. There is a need to disseminate accurate, comprehensive, and understandable MHT information online.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".