Does regulating private long-term care facilities lead to better care? A study from Quebec, Canada
Bibliographic record
Abstract
OBJECTIVE: In the province of Quebec, Canada, long-term residential care is provided by two types of facilities: publicly funded accredited facilities and privately owned facilities in which care is privately financed and delivered. Following evidence that private facilities were delivering inadequate care, the provincial government decided to regulate this industry. We assessed the impact of regulation on care quality by comparing quality assessments made before and after regulation. In both periods, public facilities served as a comparison group. DESIGN: A cross-sectional study conducted in 2010-12 that incorporates data collected in 1995-2000. SETTINGS: Random samples of private and public facilities from two regions of Quebec. PARTICIPANTS: Random samples of disabled residents aged 65 years and over. In total, 451 residents from 145 care settings assessed in 1995-2000 were compared with 329 residents from 102 care settings assessed in 2010-12. INTERVENTION: Regulation introduced by the province in 2005, effective February 2007. MAIN OUTCOME MEASURE: Quality of care measured with the QUALCARE Scale. RESULTS: After regulation, fewer small-size facilities were in operation in the private market. Between the two study periods, the proportion of residents with severe disabilities decreased in private facilities whereas it remained >80% in their public counterparts. Meanwhile, quality of care improved significantly in private facilities, while worsening in their public counterparts, even after controlling for confounding. CONCLUSIONS: The private industry now provides better care to its residents. Improvement in care quality likely results in part from the closure of small homes and change in resident case-mix.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".