Advance Care Directives: A Herzl Clinic Quality Improvement Project on Patients' perspectives
Bibliographic record
Abstract
Background: Advance Care Planning (ACP) has benefits for patients and is often optimal when done in the primary care setting. Despite the development of multiple resources and tools to support ACP discussions at our Family Medicine Teaching Clinic, the initiation and documentation of Advance Care Directives (ACD) in patients’ medical files were low and resident physicians had perceived that patients were unwilling or unprepared for ACP discussions. The goal of this project was to understand the challenges and barriers that patients and their caregivers face in initiating and discussing ACD with their primary care team. Methods: An online survey was conducted among 78 patients who are part of the Home Care program at the Herzl clinic. Participants were asked about the value placed on ACP and their preferences on various aspects surrounding the initiation of ACD discussions. Results: 25 of 78 possible responses were received. This included survey responses from 6 patients, 13 caregivers, 4 family members and 2 physicians. Our results show that patients and their caregivers value Advance Care Planning discussions (>80%). Additionally, they endorse multiple benefits of ACP for themselves, their care teams and families. Patients and caregivers prefer that medical professionals initiate and facilitate the discussions (70-80%) and are open to receive educational material to prepare for these discussions (68%). Conclusion: Patients in a frail population are willing and open to discuss advance care planning with their primary care team. Family Medicine teaching clinics can support patients’ desire to engage in ACP by providing access to education material and initiating these discussions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".