Gift of time: learning together to embed a palliative approach to care in long-term care
Bibliographic record
Abstract
BACKGROUND: Embedding a Palliative Approach to Care (EPAC) is a model that helps shift the culture in long-term care (LTC) so that residents who could benefit from palliative care are identified early. Healthcare Excellence Canada supported the implementation of EPAC in seven teams from across Canada between August 2018 and September 2019. OBJECTIVE: To identify effective strategies for supporting the early identification of palliative care needs to improve the quality of life of residents in LTC. INTERVENTION: Training methods on the EPAC model included a combination of face-to-face education (national and regional workshops), online learning (webinars and access to an online platform) and expert coaching. Each team adapted EPAC based on their organisational context and jurisdictional requirements for advance care planning. MEASURES: Teams tracked their progress by collecting monthly data on the number of residents who died, date of their most recent goals of care (GOCs) conversation, location of death and number of emergency department (ED) transfers in the last 3 months of life. Teams also shared their implementation strategies including successes, barriers and lessons. RESULTS: Implementation of EPAC required leadership support and dedicated time for changing how palliative care is perceived in LTC. Based on 409 resident deaths, 89% (365) had documented GOC conversations; 78% (318) had no transfers to the ED within the last 3 months of life; and 81% (333) died at home. A monthly review of the results showed that teams were having earlier GOC conversations with residents. Teams also reported improvements in the quality of care provided to residents and their families. CONCLUSION: EPAC was successfully adapted and adopted to the organisational contexts of homes participating in the collaborative.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".