The association between the ownership status of Ontario's long-term care homes and the quality of resident care
Bibliographic record
Abstract
There are nearly 600 long-term care (LTC) homes with approximately 70,000 residents in Ontario. LTC residents are among the frailest and most vulnerable members of society; often they are unable to articulate their needs and wishes because of multiple comorbidities and cognitive decline. The establishment and maintenance of a high quality of care in LTC facilities is vital. The purpose of this dissertation is to contribute an understanding of LTC policies and practices by exploring the relationship between ownership status and the quality of resident care in Ontario. This thesis consists of three manuscripts which explore a variety of methodologies and quality indicators: (1) A systematic review to evaluate the strength of published evidence of a relationship between LTC ownership status and quality of care; (2) An analysis of a Statistics Canada survey to determine the nurse staffing levels in Ontario's LTC facilities; and (3) An examination of the relationship between potentially avoidable hospital visits and LTC ownership status in Ontario, using administrative billing databases. The systematic literature review demonstrated conclusively that for-profit ownership negatively influenced the quality of care in the United States. There was, however, limited evidence to support a categorical statement about LTC ownership in Canada. The analysis of Statistics Canada's Residential Care Facilities Survey found that in 2000, there was no significant difference in registered nurse hours across facility types in Ontario, but for-profit facilities had a slightly higher proportion of unlicensed staff compared to not-for-profit facilities. Finally, using administrative databases, a cohort of newly admitted LTC residents in 2003 was identified to determine the rates of potentially avoidable acute care hospitalizations in Ontario's LTC homes. Residents of for-profit LTC homes were substantially more likely to be hospitalized compared to not-for-profit residents (Adjusted hazard ratio: 1.23, 95% Cl: 1.12--1.34). Conclusions. Increased risk of adverse clinical outcomes in for-profit facilities in Ontario was found despite the finding of significantly little difference in staffing levels. Future efforts are required to identify the causes of this dramatic increase in risk in FP facilities.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".