Complaints in for-profit, non-profit and public nursing homes in two Canadian provinces.
Bibliographic record
Abstract
BACKGROUND: Nursing homes provide long-term housing, support and nursing care to frail elders who are no longer able to function independently. Although studies conducted in the United States have demonstrated an association between for-profit ownership and inferior quality, relatively few Canadian studies have made performance comparisons with reference to type of ownership. Complaints are one proxy measure of performance in the nursing home setting. Our study goal was to determine whether there is an association between facility ownership and the frequency of nursing home complaints. METHODS: We analyzed publicly available data on complaints, regulatory measures, facility ownership and size for 604 facilities in Ontario over 1 year (2007/08) and 62 facilities in British Columbia (Fraser Health region) over 4 years (2004-2008). All analyses were carried out at the facility level. Negative binomial regression analysis was used to assess the association between type of facility ownership and frequency of complaints. RESULTS: The mean (standard deviation) number of verified/substantiated complaints per 100 beds per year in Ontario and Fraser Health was 0.45 (1.10) and 0.78 (1.63) respectively. Most complaints related to resident care. Complaints were more frequent in facilities with more citations, i.e., violations of the legislation or regulations governing a home, (Ontario) and inspection violations (Fraser Health). Compared with Ontario's for-profit chain facilities, adjusted incident rate ratios and 95% confidence intervals of verified complaints were 0.56 (0.27-1.16), 0.58 (0.34-1.00), 0.43 (0.21- 0.88), and 0.50 (0.30- 0.84) for for-profit single-site, non-profit, charitable, and public facilities respectively. In Fraser Health, the adjusted incident rate ratio of substantiated complaints in non-profit facilities compared with for-profit facilities was 0.18 (0.07-0.45). INTERPRETATION: Compared with for-profit chain facilities, non-profit, charitable and public facilities had significantly lower rates of complaints in Ontario. Likewise, in British Columbia's Fraser Health region, non-profit owned facilities had significantly lower rates of complaints compared with for-profit owned 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".