Advance care planning discussions among residents of long term care and designated assisted living: experience from Calgary, Alberta
Bibliographic record
Abstract
OBJECTIVES: Patients, physicians and the healthcare system are faced with the challenge of determining, and respecting, the medical wishes of an aging population. Our study sought to describe who participates in advance care planning (ACP) and decision-making for patients in long-term care and designated assisted living. METHODS: In 2008, Alberta Health Services initiated its 'Advance Care Planning: Goals of Care Designation' (Adult) policy in the Calgary zone. This policy encouraged discussions about goals of care and used a tracking form to capture these conversations. A postpolicy implementation chart review was performed at 3 time points: at baseline, at 6 months and at 18 months post implementation in long term care (LTC) and designated assisted living sites. RESULTS: 166 charts were reviewed and 90% had a documented goals of care order. Less than half of residents (47%) were documented as participating in conversations and they were less likely to participate if they had cognitive impairment and were living in LTC. Documented family participation was more prevalent in LTC (51% vs 11%). Nurses participated in 67% of documented conversations with only 34% of discussions documenting physician involvement. CONCLUSIONS: This study identifies the lack of documented resident participation in ACP in LTC. While this finding may be explained by the high prevalence of cognitive impairment in our population, it raises questions about the optimal approach to ACP in LTC. In this setting, ACP appears to be more about relational autonomy than it is about patient autonomy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".