Assessing Credibility: Quality Criteria for Patients, Caregivers, and the Public in Online Health Information—A Qualitative Study
Bibliographic record
Abstract
The increasing reliance on the Internet for health information has raised concerns about patients using unreliable and potentially harmful content. This study aimed to establish quality criteria to assist patients, caregivers, and the public in evaluating the reliability of online health information. We conducted focus group workshops with 25 participants recruited across Canada, proficient in either English or French. The participants included 13 females and 12 males, with the majority having a college or higher level of education. Through an in-depth analysis comparing various aspects, the participants determined 6 quality criteria: authorship, reliability, usefulness, accessibility, readability, and privacy & confidentiality. The findings from this study present a comprehensive list of quality criteria that will contribute to developing evidence-based quality benchmarks and policy frameworks in multiple languages. These criteria are not only valid but also well-suited to the diverse needs and preferences of patients and the public, providing a reliable framework for evaluating online health information through an evidence-based approach.
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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".