Protocol for a Cluster Randomized Trial Comparing Team-Based to Clinician-Focused Implementation of Advance Care Planning in Primary Care
Bibliographic record
Abstract
Abstract Introduction: For many patients, primary care is an appropriate setting for advance care planning (ACP). ACP focuses on what matters most to patients and ensuring health care supports patient-defined goals. ACP may involve interactions between a clinician and a patient, but for seriously ill patients ACP could be managed by a team. Methods: We are conducting a cluster randomized trial comparing team-based to clinician-focused ACP using the Serious Illness Care Program (SICP) in 42 practices recruited from 7 practice-based research networks (PBRNs). Practices were randomized to one of the two models. Patients are referred to the study after engaging in ACP in primary care. Our target enrollment is 1260 subjects. Patient data are collected at enrollment, six months and one year. Primary outcomes are patient-reported goal-concordant care and days at home. Secondary outcomes include additional patient measures, clinician/team experience, and practice-level measures of SICP implementation. Study Implementation: This trial was designed and is conducted by the Meta-network Learning and Research Center (Meta-LARC), a consortium of PBRNs focused on integrating engagement with patients, families, and other stakeholders into primary care research and practice. The trial pairs a comparative effectiveness study with implementation of a new program and is designed to balance fidelity to the assigned model with flexibility to allow each practice to adapt implementation to their environment and priorities. Our dissemination will report the results of comparing the two models and the implementation experience of the practices to create guidance for the spread of ACP in primary care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".