Developing a Quality Benchmark for Determining the Credibility of Web Health Information- a Protocol of a Gold Standard Approach
Bibliographic record
Abstract
Background: The current pandemic of COVID-19 has changed the way health information is distributed through online platforms. These platforms have played a significant role in informing patients and the public with knowledge that has changed the virtual world forever. Simultaneously, there are growing concerns that much of the information is not credible, impacting patient health outcomes, causing human lives, and tremendous resource waste. With the increasing use of online platforms, patients/the public require new learning models and sharing medical knowledge. They need to be empowered with strategies to navigate disinformation on online platforms. Methods and Design: To meet the urgent need to combat health “misinformation,” the research team proposes a structured approach to develop a quality benchmark, an evidence-based tool that identifies and addresses the determinants of online health information reliability. The specific methods to develop the intervention are the following: (1) systematic reviews: two comprehensive systematic reviews to understand the current state of the quality of online health information and to identify research gaps, (2) content analysis: develop a conceptual framework based on established and complementary knowledge translation approaches for analyzing the existing quality assessment tools and draft a unique set of quality of domains, (3) focus groups: multiple focus groups with diverse patients/the public and health information providers to test the acceptability and usability of the quality domains, (4) development and evaluation: a unique set of determinants of reliability will be finalized along with a preferred scoring classification. These items will be used to develop and validate a quality benchmark to assess the quality of online health information. Expected Outcomes: This multi-phase project informed by theory will lead to new knowledge that is intended to inform the development of a patient-friendly quality benchmark. This benchmark will inform best practices and policies in disseminating reliable web health information, thus reducing disparities in access to health knowledge and combat misinformation online. In addition, we envision the final product can be used as a gold standard for developing similar interventions for specific groups of patients or populations.
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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 | 0.476 | 0.531 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".