Quality of complementary and alternative medicine information for type 2 diabetes: a cross-sectional survey and quality assessment of websites
Bibliographic record
Abstract
BACKGROUND: The global prevalence of diabetes mellitus is projected to reach approximately 700 million by the year 2045, with roughly 90-95% of all diabetes cases being type 2 in nature. Patients with type 2 diabetes mellitus (T2DM) frequently seek information about complementary and alternative medicine (CAM) online. This study assessed the quality of publicly accessible websites providing consumer health information at the intersection of T2DM and CAM. METHODS: An online search engine (Google) was searched to identify pertinent websites containing information specific to CAM for T2DM patients, and the relevant websites were then screened with an eligibility criteria. Consumer health information found on eligible websites were then assessed for quality using the DISCERN instrument, a 16-item standardized scoring system. RESULTS: Across the 480 webpages identified, 94 unique webpages remained following deduplication, and 37 eligible webpages belonged to and were collapsed into 30 unique websites that were each assessed using the DISCERN instrument. The mean overall quality score (question 16) across all 30 assessed websites was 3.55 (SD = 0.86), and the mean summed DISCERN score was 52.40 (SD = 12.11). Eighty percent of websites presented a wide range of CAM treatment options with the associated benefits/risks of each treatment, but in 56.7% of the websites, the sources used to collect information were unreliable. CONCLUSION: This study identified, assessed, and presents findings on the quality of online CAM information for T2DM. Although there were several high scoring websites, there was variability across most of the individual DISCERN items in the assessed websites. This study highlights the importance of awareness among healthcare providers regarding the reliability of online information about CAM treatment and management options for T2DM. Healthcare providers should be aware of patients' information seeking behaviour, guide them in navigating through the content they encounter online, and provide them with resources containing trustworthy and reliable information.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".