Quality of Care for Residents Dying in Ontario Long-Term Care Facilities: Findings from a Survey of Directors of Care
Bibliographic record
Abstract
The purpose of this study was to collect information on the practice of end-of-life (EOL) care in long-term care (LTC) facilities in the Province of Ontario, Canada. A cross-sectional survey of directors of care in all licensed LTC facilities in the province was conducted between September 2003 and April 2004. Directors of care from 426 (76% response rate) facilities completed the postal survey questionnaire. The survey results identified communication problems between service providers and families, inadequate staffing levels to provide quality care to dying residents, and the need for training to improve staff skills in providing EOL care. Directors of care endorsed the use of a number of strategies that would improve the care of dying residents. Logistic regression analysis identified the eight most important items predictive of facility staff having the ability to provide quality EOL care. The findings contribute to the current discussion on policies for meeting the care needs of residents in LTC facilities until life's end.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".