O-107 Integrating advance care planning into legal practice: Development of an ACP legal toolkit
Bibliographic record
Abstract
Background The medical and legal fields coalesce around advance care planning (ACP). There is an opportunity for the legal community to be a stronger partner in the uptake of ACP beyond advance directive completion. Analysis is required of gaps in resources and supports to help lawyers promote and be engaged in ACP conversations. Aim To develop an ACP legal toolkit to help lawyers promote and be engaged in ACP communication. Methods Two workshops for researchers (5), lawyers (17), clinicians (3), and other stakeholders (8) from the legal and medical communities are being held in Alberta, Canada to discuss lawyers’ roles, needs and barriers regarding ACP. These workshops will bring together key stakeholder groups who have been working in parallel and have a keen interest in improving the uptake of ACP, but have otherwise had few opportunities to connect e.g. the Canadian Bar Association, Legal Aid, legal education organisations, academics, government representatives, and patient advisors. The intended outcomes of the meetings are to establish the contents of an ACP legal toolkit, establish roles and responsibilities around compiling the toolkit, and to develop a plan for dissemination. A core group (10) from these meetings will attend a follow up meeting about implementation. Results A description of the meeting results, the ACP legal toolkit, its compilation, development and dissemination will be presented
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".