Bibliographic record
Abstract
OBJECTIVES: The average percentage of residents restrained in nursing homes is approximately 20%. Facilities that do not meet Health Care Financing Administration standards for restraint use may be issued a deficiency citation. This article investigates which structure and process factors of nursing homes are associated with a deficiency citation for restraint use. METHODS: Nationally representative data from the 1997 On-line Survey Certification of Automated Records are used, first, to provide descriptive analyses, and second, for logistic regression analyses of structure and process factors associated with a deficiency citation for restraint use. RESULTS: A total of 2,321 facilities were found to have at least one restraint deficiency citation, and 14,703 had none. After controlling for seven other key variables, five structural factors and six process factors are significant. The structural factors--larger bed size, for-profit ownership, and hospital based--were significantly associated with a higher likelihood of a deficiency citation for restraint use; whereas higher numbers of full-time equivalent specialists per resident and nurse aide training were significantly associated with a lower likelihood. The process factors--suctioning therapy, pain management, and bladder training--were significantly associated with a lower likelihood of a deficiency citation for restraint use; whereas intravenous therapy, higher use of catheters, and physical restraints were significantly associated with a higher likelihood of a deficiency citation. DISCUSSION: This analysis establishes linkages between structures and processes and the outcome of a deficiency citation for restraint use. The structural results may have some utility for regulators. They could be used to develop a specific program to target facilities most commonly found to have inappropriate restraint use. The process results may have some utility for providers who could use the information to target residents for review of inappropriate restraint use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".