The Impact of Ontario's End-of-Life Care Strategy on End-of-Life Care in the Community
Bibliographic record
Abstract
This article describes the impacts of the Ministry of Health and Long-Term Care's End-of-Life Care Strategy on the quality of end-of-life (EOL) care services delivered by home care providers across the province of Ontario. We compared key home care services one year before the strategy's implementation with those one year after. In addition, we conducted a qualitative survey of all community care access centres, the main providers of home care, and nearly all EOL Care Network directors to assess improvements to EOL care at the system and client level. Results showed that the number of clients of EOL care served increased by 3,537 over the baseline year. Moreover, the total number of nursing visits, shift nursing hours and personal support hours increased by 26%, 31% and 47%, respectively, compared with the baseline year. The qualitative analysis indicated that increased collaborations and communication have enhanced integration, coordination and consistency of EOL care. Anecdotally, clients and families feel more supported navigating the healthcare system, and more of their wishes are being met. The strategy appeared to improve EOL care on multiple levels. However, several barriers and challenges remain. Further investments and research are needed to achieve reliable quality EOL care for all Ontarians.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".