Relationship between Regulatory Status, Quality of Care, and Three‐Year Mortality in Canadian Residential Care Facilities: A Longitudinal Study
Bibliographic record
Abstract
OBJECTIVES: To compare the mortality rate in regulated and unregulated facilities, controlling for confounding variables, and investigate the effect of care quality on residents' length of survival. DATA SOURCES/STUDY SETTING: At baseline, subjects were assessed in their living environment with respect to their functional autonomy, cognitive abilities, and quality of care. Vital status, disease-related information, and hospitalization data were retrieved three years later from the subjects' medical files. STUDY DESIGN: A three-year follow-up study of 299 residents from 88 long-term care facilities located in the province of Quebec, Canada. The effect of regulatory status and quality of care on length of survival was investigated by means of multivariable Cox proportional hazards regression models, from both traditional and competing risks perspectives. PRINCIPAL FINDINGS: Controlling for age, comorbidity, and baseline functional abilities, a resident's length of survival is not significantly influenced by the regulatory status of the facility in which he or she lived at baseline. However, residents with poor quality ratings at baseline had shorter survival times than those provided with good care. Median survival was 28 months among residents classified as receiving inadequate care compared to 41 months for those adequately cared for (p = 0.0217). CONCLUSIONS: The study suggests that quality of care has a much stronger influence on resident outcomes than regulation per se. This finding underscores the relevance of testing innovative interventions aimed at improving the quality of care provided in long-term care facilities, regardless of their regulatory status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".