Advance Care Directives : A Herzl Clinic Quality Improvement Project
Bibliographic record
Abstract
Background: Advance Care Planning has benefits for patients and is often optimal when done in the primary care setting. Unfortunately, it does not occur frequently or routinely. The goal of this project was to understand the challenges and barriers that residents at a Family Medicine training site face in initiating and discussing Advance Care Directives. Methods: An online survey was conducted among 50 Family Medicine residents at the Herzl clinic. Participants were asked about their experience, their comfort level, and their challenges with Advance Care Planning discussions. A focus group with 12 Family Medicine residents further probed, through open-ended questions, the specific challenges they have faced during Advance Care Planning and ideas to address them. Results: The online survey and focus group identified that most residents perceived a lack of time, inadequate training, and poor uptake of available tools as barriers to have Advance Care Planning discussions in a community setting. Residents also felt that patients were inadequately prepared for these discussions. For improvement, most residents suggested to increase the variety of teaching modalities, to dedicate time for these discussions and to prioritize in-person discussions. Conclusion: The residents in Family Medicine face many challenges and barriers to having Advance Care Directives discussions with their patients but were able to provide avenues for improvement.
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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 | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".