Publication of Quality Report Cards and Trends in Reported Quality Measures in Nursing Homes
Bibliographic record
Abstract
OBJECTIVE: To examine associations between nursing homes' quality and publication of the Nursing Home Compare quality report card. DATA SOURCES/STUDY SETTINGS: Primary and secondary data for 2001-2003: 701 survey responses of a random sample of nursing homes; the Minimum Data Set (MDS) with information about all residents in these facilities, and the Nursing Home Compare published quality measure (QM) scores. STUDY DESIGN: Survey responses provided information on 20 specific actions taken by nursing homes in response to publication of the report card. MDS data were used to calculate five QMs for each quarter, covering a period before and following publication of the report. Statistical regression techniques were used to determine if trends in these QMs have changed following publication of the report card in relation to actions undertaken by nursing homes. PRINCIPAL FINDINGS: Two of the five QMs show improvement following publication. Several specific actions were associated with these improvements. CONCLUSIONS: Publication of the Nursing Home Compare report card was associated with improvement in some but not all reported dimensions of quality. This suggests that report cards may motivate providers to improve quality, but it also raises questions as to why it was not effective across the board.
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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 | 0.049 | 0.297 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".