Association between Traditional Nursing Home Quality Measures and Two Sources of Nursing Home Complaints
Bibliographic record
Abstract
OBJECTIVE: To test for an association between traditional nursing home quality measures and two sources of resident- and caregiver-derived nursing home complaints. DATA SOURCES: Nursing home complaints to the North Carolina Long-Term Care Ombudsman Program and state certification agency from October 2002 through September 2006 were matched with Online Survey Certification and Reporting data and Minimum Data Set Quality Indicators (MDS-QIs). STUDY DESIGN: We examine the association between the number of complaints filed against a facility and measures of inspection violations, staffing levels, and MDS-QIs. DATA EXTRACTION: One observation per facility per quarter is constructed by matching quarterly data on complaints to OSCAR data from the same or most recent prior quarter and to MDS-QIs from the same quarter. One observation per inspection is obtained by matching OSCAR data to complaint totals from both the same and the immediate prior quarter. PRINCIPAL FINDINGS: There is little relationship between MDS-QIs and complaints. Ombudsman complaints and inspection violations are generally unrelated, but there is a positive relationship between state certification agency complaints and inspection violations. CONCLUSIONS: Ombudsman and state certification agency complaint data are resident- and caregiver-derived quality measures that are distinctive from and complement traditional quality measures.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".