The quality, suitability, and readability of web-based resources on endometriosis-associated dyspareunia: A systematic review
Bibliographic record
Abstract
People commonly and increasingly rely on the internet to search for health information, including those related to endometriosis-associated dyspareunia. Yet the content of such websites may be of variable accuracy and quality. This review aims to evaluate the quality, readability, and suitability of web-based resources on endometriosis-associated dyspareunia for patients. We searched 3 databases - Google, Bing, and Yahoo - to identify websites related to endometriosis-associated dyspareunia. Two independent reviewers screened the search results against inclusion and exclusion criteria. Another set of two reviewers evaluated the selected websites using validated measurement instruments. Out of 450 websites, 21 met the inclusion criteria and were evaluated. More than half of the websites had information on content updates, reported on authorship, or disclosed sponsorship information. The mean quality and suitability scores were 47.5 (SD = 13.3) and 65.2 (SD = 13.6) respectively, thus suggesting generally adequate quality and suitability levels. However, the mean readability scores exceeded the recommended level for health-related websites. The poor readability of the websites might limit accessibility for a significant proportion of patients with low educational levels. The findings of this review have implications for designing high-quality, readable and up-to-date web interventions for people who rely on web platforms as an alternative or complementary source of health information on dyspareunia.
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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 | 0.012 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".