The quality of online consumer health information at the intersection of complementary and alternative medicine and arthritis
Bibliographic record
Abstract
BACKGROUND: Complementary and alternative medicine (CAM) use is prevalent among patients living with arthritis. Such patients often seek information online, for the purpose of gaining a second opinion to their healthcare provider or even self-medication. Little is known about the quality of web-based consumer health information at the intersection of CAM and arthritis; thus, investigating the quality of websites containing this information was the purpose of this study. METHODS: Four unique search terms were searched on Google across four English-speaking countries. We assessed the first 20 results of each search, including them if they contained CAM consumer health information for the treatment and/or management of arthritis. Eligible websites were assessed in duplicate using the DISCERN instrument, which consists of 16-items designed to assess quality. RESULTS: Of total of 320 webpages, 239 were duplicates, and a total of 38 unique websites were deemed eligible and assessed using the DISCERN instrument. The mean summed DISCERN scores across all websites was 55.53 (SD = 9.37). The mean score of the overall quality of each website was 3.71 (SD = 0.63), thus the majority of websites are ranked as slightly above 'fair' quality. CONCLUSION: Eligible websites generally received scores better than 'moderate' in terms of overall quality. Several shortcomings included a lack of transparency surrounding references used and underreporting of risks associated with treatment options. These results suggest that health providers should be vigilant of the variable quality of information their patients may be accessing online and educate them on how to identify high quality resources.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".