How do Lawyers Assist Their Clients With Advance Care Planning? Findings From a Cross-Sectional Survey of Lawyers in Alberta
Bibliographic record
Abstract
Advance care planning (ACP) is the process of thinking about, discussing and documenting one’s preferences for future health care. ACP has important benefits: people who have a written directive are more likely to receive care that accords with their preferences, have fewer hospitalizations, and die in their preferred location. This article focuses on the important role that legal professionals have in advising and assisting clients with ACP. Studies report that people who have a written advance care plan are more likely to have received assistance in preparing the document from a lawyer than from a doctor. Yet virtually no research engages with the legal profession to understand lawyers’ attitudes, beliefs, and practices in this important area. This article starts to fill this gap by reporting the findings of a survey of lawyers in the province of Alberta. The results reveal lawyers’ practices in relation to ACP, their perceptions of their professional role and factors that support or hinder lawyers in working with clients on ACP, and their preferences for resources to assist them in helping their clients. To the authors’ knowledge, this is the first survey of lawyers on their practices in relation to ACP.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".