Identifying Star Performers: The Relationship Between Ambitious Targets and Nursing Home Quality Improvement
Bibliographic record
Abstract
Setting Targets--Achieving Results (STAR) is a Web-based tool that helps nursing home leadership select annual performance goals, or targets, for a subset of publicly reported quality measures. Previous results demonstrate that nursing homes whose staff implement STAR targets demonstrate greater improvement on the related outcomes. In this analysis, the authors hypothesized that nursing homes whose staff select the most ambitious targets (reflecting large improvement over their current performance) may be more successful in their related quality improvement efforts than homes with less-ambitious targets (reflecting lesser improvement). The authors analyzed data from 7,091 Medicare- or Medicaid-certified nursing homes that set STAR targets in 2005 or 2006 for two quality measures: the proportion of residents who were physically restrained daily and the proportion of high-risk residents with pressure ulcers. Targets were classified as ambitious or less ambitious based on the 75th and 50th rank-ordered percentiles, respectively. Improvement was calculated using four-quarter averages for baseline (the year ending when the target was set) and remeasurement (the subsequent year). The results indicate that nursing homes with ambitious targets demonstrate greater improvement than their peers selecting less-ambitious targets. With limited federal and local resources to assist providers with quality improvement, target values may be a used as a "flag" to help agencies allocate scarce resources to nursing homes committed to quality improvement efforts and with the organizational capacity to improve.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".