Medical Information on the Internet: A Tool for Measuring Consumer Perception of Quality Aspects
Bibliographic record
Abstract
BACKGROUND: Most of adult Internet users have searched for health information on the Internet. The Internet has become one of the most important sources for health information and treatment advice. In most cases, the information found is not verified with a medical doctor, but judged by the "online-diagnosers" independently. Facing this situation, public health authorities raise concern over the quality of medical information laypersons can find on the Internet. OBJECTIVE: The objective of the study was aimed at developing a measure to evaluate the credibility of websites that offer medical advice and information. The measure was tested in a quasi-experimental study on two sleeping-disorder websites of different quality. METHODS: There were 45 survey items for rating the credibility of websites that were tested in a quasi-experimental study with a random assignment of 454 participants to either a high- or a low-quality website exposure. Using principal component analysis, the original items were reduced to 13 and sorted into the factors: trustworthiness, textual deficits of the content, interferences (external links on the Web site), and advertisements. The first two factors focus more on the provided content itself, while the other two describe the embedding of the content into the website. The 45 survey items had been designed previously using exploratory observations and literature research. RESULTS: The final scale showed adequate power and reliability for all factors. The loadings of the principal component analysis ranged satisfactorily (.644 to .854). Significant differences at P<.001 were found between the low- and high-quality groups. Advertisements on the website were rated as disturbing in both experimental conditions, meaning that they do not differentiate between good and bad information. CONCLUSIONS: The scale reliably distinguished high- and low-quality of medical advice given on websites.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".