An International Comparison of Web-based Reporting About Health Care Quality: Content Analysis
Bibliographic record
Abstract
BACKGROUND: On more and more websites, consumers are provided with public reports about health care. This move toward provision of more comparative information has resulted in different information types being published that often contain contradictory information. OBJECTIVE: The objective was to assess the current state of the art in the presentation of online comparative health care information and to compare how the integration of different information types is dealt with on websites. The content analysis was performed in order to provide website managers and Internet researchers with a resource of knowledge about presentation formats being applied internationally. METHODS: A Web search was used to identify websites that contained comparative health care information. The websites were systematically examined to assess how three different types of information (provider characteristics and services, performance indicators, and health care user experience) were presented to consumers. Furthermore, a short survey was disseminated to the reviewed websites to assess how the presentation formats were selected. RESULTS: We reviewed 42 websites from the following countries: Australia, Canada, Denmark, Germany, Ireland, the Netherlands, Norway, the United Kingdom, the United States, and Sweden. We found the most common ways to integrate different information types were the two extreme options: no integration at all (on 36% of the websites) and high levels of integration in single tables on 41% of the websites). Nearly 70% of the websites offered drill down paths to more detailed information. Diverse presentation approaches were used to display comparative health care information on the Internet. Numbers were used on the majority of websites (88%) to display comparative information. CONCLUSIONS: Currently, approaches to the presentation of comparative health care information do not seem to be systematically selected. It seems important, however, that website managers become aware of the complexities inherent in comparative information when they release information on the Web. Important complexities to pay attention to are the use of numbers, the display of contradictory information, and the extent of variation among attributes and attribute levels. As for the integration of different information types, it remains unclear which presentation approaches are preferable. Our study provides a good starting point for Internet research to further address the question of how different types of information can be more effectively presented to consumers.
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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 | 0.070 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".