Complementary and Alternative Medicine on Wikipedia: Opportunities for Improvement
Bibliographic record
Abstract
Wikipedia, a free and collaborative Internet encyclopedia, has become one of the most popular sources of free information on the Internet. However, there have been concerns over the quality of online health information, particularly that on complementary and alternative medicine (CAM). This exploratory study aimed to evaluate several page attributes of articles on CAM in the English Wikipedia. A total of 97 articles were analyzed and compared with eight articles of broad categories of therapies in conventional medicine using the Mann-Whitney U test. Based on the Wikipedia editorial assessment grading, 4% of the articles attained "good article" status, 34% required considerable editing, and 56% needed substantial improvements in their content. The median daily access of the articles over the previous 90 days was 372 (range: 7-4,214). The median word count was 1840 with a readability of grade 12.7 (range: 9.4-17.7). Medians of word count and citation density of the CAM articles were significantly lower than those in the articles of conventional medicine therapies. In conclusion, despite its limitations, the general public will continue to access health information on Wikipedia. There are opportunities for health professionals to contribute their knowledge and to improve the accuracy and completeness of the CAM articles on Wikipedia.
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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 | 0.030 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".