Public Reporting of Hospital Infection Rates: Ranking the States on Credibility and User Friendliness
Bibliographic record
Abstract
Health-care associated infections ("HAIs") kill about 100,000 people annually; most are preventable, but many hospitals have not aggressively addressed the problem. In response, twenty-five states and the U.S. Department of Health and Human Services require public reporting of hospital infection rates for at least some types of infections, and other states and private entities are implementing such reporting. The websites and related reports vary widely in ease of access, ease of use, usefulness of information, timeliness of updates, and credibility. We report on work in progress, in which we assess the quality and suitability of different state websites and reports for different target audiences (ordinary consumers; physicians, and infection control professionals) and the extent to which they meet best practices for online communication, including Stanford's "Fogg" Guidelines for Web Credibility and user-friendliness metrics developed by other researchers. We find wide variation in quality, and substantial correlation between measures of website credibility and user-friendliness. We identify ways to improve usability, usefulness, and tailoring for information to different target audiences. Our analysis suggests that the "one website (and report format) fits all users" model may not work well in delivering complex, technical information to users with widely varying needs and sophistication.
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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 | 0.023 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".