Quality of Web Information About Palliative Care on Websites from the United States and Japan: Comparative Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Patients and their families are able to obtain information about palliative care from websites easily nowadays. However, there are concerns on the accuracy of information on the Web and how up to date it is. OBJECTIVE: The objective of this study was to elucidate problematic points of medical information about palliative care obtained from websites, and to compare the quality of the information between Japanese and US websites. METHODS: We searched Google Japan and Google USA for websites relating to palliative care. We then evaluated the top 50 websites from each search using the DISCERN and LIDA instruments. RESULTS: We found that Japanese websites were given a lower evaluation of reliability than US websites. In 3 LIDA instrument subcategories-engagability (P<.001), currency (P=.001), and content production procedure (P<.001)-US websites scored significantly higher and had large effect sizes. CONCLUSIONS: Our results suggest that Japanese websites have problems with the frequency with which they are updated, their update procedures and policies, and the scrutiny process the evidence must undergo. Additionally, there was a weak association between search ranking and reliability, and simultaneously we found that reliability could not be assessed by search ranking alone.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".