The QUEST for quality online health information: validation of a short quantitative tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Online health information is unregulated and can be of highly variable quality. There is currently no singular quantitative tool that has undergone a validation process, can be used for a broad range of health information, and strikes a balance between ease of use, concision and comprehensiveness. To address this gap, we developed the QUality Evaluation Scoring Tool (QUEST). Here we report on the analysis of the reliability and validity of the QUEST in assessing the quality of online health information. METHODS: The QUEST and three existing tools designed to measure the quality of online health information were applied to two randomized samples of articles containing information about the treatment (n = 16) and prevention (n = 29) of Alzheimer disease as a sample health condition. Inter-rater reliability was assessed using a weighted Cohen's kappa (κ) for each item of the QUEST. To compare the quality scores generated by each pair of tools, convergent validity was measured using Kendall's tau (τ) ranked correlation. RESULTS: The QUEST demonstrated high levels of inter-rater reliability for the seven quality items included in the tool (κ ranging from 0.7387 to 1.0, P < .05). The tool was also found to demonstrate high convergent validity. For both treatment- and prevention-related articles, all six pairs of tests exhibited a strong correlation between the tools (τ ranging from 0.41 to 0.65, P < .05). CONCLUSIONS: Our findings support the QUEST as a reliable and valid tool to evaluate online articles about health. Results provide evidence that the QUEST integrates the strengths of existing tools and evaluates quality with equal efficacy using a concise, seven-item questionnaire. The QUEST can serve as a rapid, effective, and accessible method of appraising the quality of online health information for researchers and clinicians alike.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it