Use of physical restraints and antipsychotic medications in nursing homes: a cross‐national study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: This study compares inter- and intra-country differences in the prevalence of physical restraints and antipsychotic medications in nursing homes, and examines aggregated resident conditions and organizational characteristics correlated with these treatments. METHODS: Population-based, cross-sectional data were collected using a standardized Resident Assessment Instrument (RAI) from 14,504 long-term care facilities providing nursing home level services in five countries participating in the interRAI consortium, including Canada, Finland, Hong Kong (Special Administrative Region, China), Switzerland, and the United States. Facility-level prevalence rates of physical restraints and antipsychotic use were examined both between and within the study countries. RESULTS: The prevalence of physical restraint use varied more than five-fold across the study countries, from an average 6% in Switzerland, 9% in the US, 20% in Hong Kong, 28% in Finland, and over 31% in Canada. The prevalence of antipsychotic use ranged from 11% in Hong Kong, between 26-27% in Canada and the US, 34% in Switzerland, and nearly 38% in Finland. Within each country, substantial variations existed across facilities in both physical restraint and antipsychotic use rates. In all countries, neither facility case mix nor organizational characteristics were particularly predictive of the prevalence of either treatment. CONCLUSIONS: There exists large, unexplained variability in the prevalence of physical restraint and antipsychotic use in nursing home facilities both between and within countries. Since restraints and antipsychotics are associated with adverse outcomes, it is important to understand the idiosyncratic factors specific to each country that contribute to variation in use rates.
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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 | 0.001 | 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 it