Evaluating the Feasibility of Web-Monitoring Methodology for Measuring Exposure to Online Cancer Misinformation
Bibliographic record
Abstract
Unlabelled: Understanding the impact of online cancer misinformation exposure on health outcomes is an area of growing concern, but few methods exist to objectively measure this exposure. The primary aim of this paper is to describe the lessons learned in using web-monitoring software to measure exposure to online cancer misinformation among patients with cancer. These lessons learned emerged from our experience conducting a prospective pilot study from October 2022 to August 2023 wherein we adopted commercially available web-monitoring software to capture cancer-related web content. A total of 56 patients with cancer completed a baseline survey, and 17 of these participants installed web-monitoring software on their personal computer for 30 days and completed a follow-up survey. We use implementation outcomes to describe the feasibility of this methodological approach using lessons learned in 3 topic areas, namely data quality, software implementation, and participant acceptability. We found the web-monitoring data to be appropriate for our research aim to objectively measure cancer misinformation exposure, although compatibility issues with social media websites and mobile devices negatively impacted data quality. A complex installation process negatively impacted implementation and caused an unknown number of participants to drop out after the baseline survey. Among participants who completed the study, reported acceptability of web-monitoring software for research purposes was high, though potentially biased by selective retention. This pilot study testing web-monitoring software for research purposes among patients with cancer demonstrates high acceptability but low feasibility due to implementation barriers. We propose practical solutions to address these barriers and believe the lessons learned here offer a promising foundation for improving methods to objectively measure patient exposure to online cancer information. Future studies should focus on exploring perceptions of web-monitoring among nonparticipants, considering alternative approaches, and expanding web-monitoring to include mobile devices.
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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 | 0.125 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".