Assessing the Quality of Online Health Information About Breast Cancer from Chinese Language Websites: Quality Assessment Survey
Bibliographic record
Abstract
BACKGROUND: In China, the internet has become one of the most important ways to obtain information about breast cancer. However, quantitative evaluations of the quality of Chinese health websites and the breast cancer treatment information they publish are lacking. OBJECTIVE: This study aimed to evaluate the quality of Chinese breast cancer websites and the value, suitability, and accuracy of the breast cancer treatment information they publish. METHODS: Chinese breast cancer health websites were searched and manually screened according to their Alexa and Baidu search engine rankings. For each website included in the survey, which was conducted on April 8, 2019, the three most recently published papers on the website that met the inclusion criteria were included for evaluation. Three raters assessed all materials using the LIDA, DISCERN, and Suitability Assessment of Materials (SAM) tools and the Michigan Checklist. Data analysis was completed with the Statistical Package for Social Sciences (SPSS) version 20.0 and Microsoft Excel 2010. RESULTS: This survey included 20 Chinese breast cancer websites and 60 papers on breast cancer treatment. The LIDA tool was used to evaluate the quality of the 20 websites. The LIDA's scores of the websites (mean=54.85, SD 3.498; total possible score=81) were low. In terms of the layout, color scheme, search facility, browsing facility, integration of nontextual media, submission of comments, declaration of objectives, content production method, and robust method, more than half of the websites scored 0 (never) or 1 (sometimes). For the online breast cancer treatment papers, the scores were generally low. Regarding suitability, 32 (53.33%) papers were evaluated as presenting unsuitable material. Regarding accuracy, the problems were that the papers were largely not original (44/60, 73%) and lacked references (46/60, 77%). CONCLUSIONS: The quality of Chinese breast cancer websites is poor. The color schemes, text settings, user comment submission functions, and language designs should be improved. The quality of Chinese online breast cancer treatment information is poor; the information has little value to users, and pictorial information is scarcely used. The online breast cancer treatment information is accurate but lacks originality and references. Website developers, governments, and medical professionals should play a full role in the design of health websites, the regulation of online health information, and the use of online health information.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".