Advance Care Planning in Primary Care: A Step toward Normalizing the Conversation
Bibliographic record
Abstract
Despite the number of advance care planning (ACP) conversation guides and tools, ACP conversations are not common in healthcare. In this quality improvement project, we took a different approach and applied complex adaptive systems theory to develop an intervention that emerged from the users (family physicians) themselves - a standardized e-form with prompts. By listening to the users, we were able to integrate ACP best practices, including shifting the focus of ACP conversations from treatment decisions to patient values, in a way that met both users' and patients' needs, addressed barriers and will help normalize ACP conversations in primary care. The intervention was designed for any patient and family physician and may have utility for other family practice groups.
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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 | 0.063 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".