Dietary and herbal supplements for fatigue: A quality assessment of online consumer health information
Bibliographic record
Abstract
BACKGROUND: The Internet is increasingly utilized by patients to acquire information about dietary and herbal supplements (DHSs). Previously published studies assessing the quality of websites providing consumer health information about DHSs have been found to contain inaccuracies and misinformation that may compromise patient safety.. The present study assessed the quality of online DHSs consumer health information for fatigue. METHODS: Six unique search terms were searched on Google, each relating to fatigue and DHSs, across four countries. Across 480 websites identified, 48 were deemed eligible and were quality assessed using the DISCERN instrument, a standardized index of the quality of consumer health information. RESULTS: Across 48 eligible websites, the mean summed score was 47.64 (SD = 10.38) and the mean overall rating was 3.06 (SD = 0.90). Commercial sites were the most numerous in quantity, but contained information of the poorest quality. In general, websites lacked discussion surrounding uncertainty of information, describing what would happen if no treatment was used, and how treatment choices affect overall quality of life. CONCLUSION: Physicians and other healthcare professionals should be aware of the high variability in the quality of online information regarding the use of DHSs for fatigue and facilitate open communication with patients to guide them towards reliable online sources.
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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 | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".