Comparing Primary Care Clinician-Focused Versus Team-Based Implementation of Advance Care Planning: Protocol for a Cluster-Randomized Control Trial, United States and Canada, 2019-2022
Bibliographic record
Abstract
For people with serious chronic conditions, healthcare that defaults to all available treatments without considering patient preferences risks harms that may exceed benefits. Advance care planning (ACP) has the potential to align healthcare with what is important to patients and maximize quality of life. While primary care is where most people receive most of their care, engaging patients in ACP is not routine in primary care given competing demands and limited resources. Primary care clinicians, patients, and families agree that it is preferred to make plans before there is a medical crisis. The research team's goal was to make ACP routine in primary care and to "move it upstream" so that it included improving the quality of the last years of life as well as respecting wishes for end of life care. This study included a comparative effectiveness trial of team-based versus individual clinician-focused ACP in primary care practices. The research team adapted Ariadne Labs' Serious Illness Care Program (SICP) and aimed to determine if a team approach produces better patient outcomes and explore factors influencing implementation of ACP across practices. Seven practice-based research networks (PBRNs) in the United States and Canada randomized their primary care practices to team-based or individual clinician-focused versions of SICP. Team members and clinicians completed training, and implementation was supported through practice facilitation. Consented patient participants completed a baseline survey after initial conversations and follow-up surveys at 6 and 12 months later. Forty practices (21 team, 19 clinician) completed training and referred patients to the study. Half of the practices were rural, 80 percent were family medicine, and 33 percent were medical residency training sites. 535 healthcare staff completed training. Both arms trained primary care providers; the team arm also trained nurses, medical assistants, and other roles. 1,321 patients and care partners were referred; and 917 consented and were enrolled (455 from team practices, 462 from clinician). Data from 802 patients were included in the primary analyses. Qualitative implementation data was collected during practice facilitation and from practice interviews. This collection includes quantitative data collected from primary care practices (DS1) and team members and clinicians (DS2) from study sites located in the United States.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.060 | 0.008 |
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".