The Impact of the Long-term Care Homes Act and Public Reporting on Physical Restraint and Potentially Inappropriate Antipsychotic Use in Ontario’s Long-term Care Homes
Bibliographic record
Abstract
BACKGROUND: We report on the impact of two system-level policy interventions (the Long-Term Care Homes Act [LTCHA] and Public Reporting) on publicly reported physical restraint use and non-publicly reported potentially inappropriate use of antipsychotics in Ontario, Canada. METHODS: We used interrupted time series analysis to model changes in the risk-adjusted use of restraints and antipsychotics before and after implementation of the interventions. Separate analyses were completed for early ([a] volunteered 2010/2011) and late ([b] volunteered March 2012; [c] mandated September 2012) adopting groups of Public Reporting. Outcomes were measured using Resident Assessment Instrument Minimum Data Set (RAI-MDS) data from January 1, 2008 to December 31, 2014. RESULTS: For early adopters, enactment of the LTCHA in 2010 was not associated with changes in physical restraint use, while Public Reporting was associated with an increase in the rate (slope) of decline in physical restraint use. By contrast, for the late-adopters of Public Reporting, the LTCHA was associated with significant decreases in physical restraint use over time, but there was no significant increase in the rate of decline associated with Public Reporting. As the LTCHA was enacted, potentially inappropriate use of antipsychotics underwent a rapid short-term increase in the early volunteer group, but, over the longer term, their use decreased for all three groups of homes. CONCLUSIONS: Public Reporting had the largest impact on voluntary early adopters while legislation and regulations had a more substantive positive effect upon homes that delayed public reporting.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".