Web-Based Public Reporting as a Decision-Making Tool for Consumers of Long-Term Care in the United States and the United Kingdom: Systematic Analysis of Report Cards
Bibliographic record
Abstract
BACKGROUND: Report cards can help consumers make an informed decision when searching for a long-term care facility. OBJECTIVE: This study aims to examine the current state of web-based public reporting on long-term care facilities in the United States and the United Kingdom. METHODS: We conducted an internet search for report cards, which allowed for a nationwide search for long-term care facilities and provided freely accessible quality information. On the included report cards, we drew a sample of 1320 facility profiles by searching for long-term care facilities in 4 US and 2 UK cities. Based on those profiles, we analyzed the information provided by the included report cards descriptively. RESULTS: We found 40 report cards (26 in the United States and 14 in the United Kingdom). In total, 11 of them did not state the source of information. Additionally, 7 report cards had an advanced search field, 24 provided simplification tools, and only 3 had a comparison function. Structural quality information was always provided, followed by consumer feedback on 27 websites, process quality on 15 websites, prices on 12 websites, and outcome quality on 8 websites. Inspection results were always displayed as composite measures. CONCLUSIONS: Apparently, the identified report cards have deficits. To make them more helpful for users and to bring public reporting a bit closer to its goal of improving the quality of health care services, both countries are advised to concentrate on optimizing the existing report cards. Those should become more transparent and improve the reporting of prices and consumer feedback. Advanced search, simplification tools, and comparison functions should be integrated more widely.
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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 | 0.160 | 0.471 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.035 | 0.033 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".