Improving End-of-Life Care and Advance Care Planning for Frail Older Adults in Canada
Bibliographic record
Abstract
We present five Key Concepts that describe priorities for improving end-of-life care for frail older adults in Canada, and recommendations based on each Key Concept. Key Concept #1: Our end-of-life care system is focused on cancer, not frailty. Key Concept #2: We need better strategies to systematically identify frail older adults who would benefit from a palliative approach. Key Concept #3: The majority of palliative and end-of-life care will be, and should be, provided by clinicians who are not palliative care specialists. Key Concept #4: Organizational change and innovative funding models could deliver far better end-of-life care to frail individuals for less than we are currently spending. Key Concept #5: Improving the quality and quantity of advance care planning for frail older adults could reduce unwanted intensive care and costs at the end of life, and improve the experience for individuals and family members alike.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".