Exploring the Quality Performance of Ethno-Specific and Mainstream Not-For-Profit Long-Term Care Homes in Ontario
Bibliographic record
Abstract
The purpose of this retrospective study was to determine how ethno-specific not-for-profit long-term care (LTC) homes in Ontario perform in comparison to mainstream not-for-profit LTC homes using nine RAI-MDS 2.0 quality indicators. Publicly available data from three sources: the Canadian Institute for Health Information "Your Health System: In Depth" database from the Continuing Care Reporting System, data on Ontario ethno-specific not-for-profit LTC homes from the Home and Community Care Support Services, and data on home and ownership records from the Ministry of Long-Term Care “Public Reporting” website, between 2017-2022, were retrieved and analyzed. Descriptive analysis suggests that for all quality indicators, except for improved physical functioning, ethno-specific not-for-profit LTC homes performed better, with fewer residents experiencing adverse health outcomes. Significance testing suggests that four quality indicators were statistically different between not-for-profit ethno-specific and mainstream LTC homes. Specifically, ethno-specific LTC homes had a smaller percentage of residents experiencing pain, falls in the last 30 days, and worsening depressive moods, while mainstream LTC homes had a higher percentage of residents experiencing improved physical functioning. The study findings aim to inform future research on interventions and policy adaptations to enhance the overall quality of care for culturally, religiously, and ethnically diverse older adults living in Ontario’s LTC homes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".