Assessing the Reliability and Validity of Principles for Health-Related Information on Social Media (PRHISM) for Evaluating Breast Cancer Treatment Videos on YouTube: Instrument Validation Study
Bibliographic record
Abstract
Background: There is breast cancer-related medical information on social media, but there is no established method for objectively evaluating the quality of this information. Principles for Health-Related Information on Social Media (PRHISM) is a newly developed tool for objectively assessing the quality of health-related information on social media; however, there have been no reports evaluating its reliability and validity. Objective: The purpose of this study was to statistically examine the reliability and validity of PRHISM using videos about breast cancer treatment on YouTube (Google). Methods: In total, 60 YouTube videos were selected on January 5, 2024, with the Japanese words for "breast cancer," "treatment," and "chemotherapy," and assessed by 6 Japanese physicians with expertise in breast cancer. These evaluators independently evaluated the videos using PRHISM and an established tool for assessing the quality of health-related information, DISCERN, as well as through subjective assessments. We calculated interrater and intrarater agreement among evaluators with CIs, measuring agreement using weighted Cohen kappa. Results: The interrater agreement for PRHISM overall quality was κ=0.52 (90% CI 0.49-0.55), indicating that the expected level of agreement, statistically defined by the lower limit of the 90% CI exceeding 0.53, was not achieved. However, PRHISM demonstrated higher agreement compared with DISCERN overall quality, which had a κ=0.45 (90% CI 0.41-0.48). In terms of validity, the intrarater agreement between PRHISM and subjective assessments by breast experts was κ=0.37 (95% CI 0.14-0.60), while DISCERN showed an agreement of κ=0.27 (95% CI 0.07-0.48), indicating fair agreement and no significant difference in validity. Conclusions: PRHISM has demonstrated sufficient reliability and validity for evaluating the quality of health-related information on YouTube, making it a promising new metric. To further enhance objectivity, it is necessary to explore the use of artificial intelligence and other approaches.
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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 | 0.059 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".