Advance Care Planning: Understanding Clinical Routines and Experiences of Interprofessional Team Members in Diverse Health Care Settings
Bibliographic record
Abstract
BACKGROUND: Interprofessional health care team members consider advance care planning (ACP) to be important, yet gaps remain in systematic clinical routines to support ACP. A clearer understanding of the interprofessional team members' perspectives on ACP clinical routines in diverse settings is needed. METHODS: One hundred eighteen health care team members from community-based clinics, long-term care facilities, academic clinics, federally qualified health centers, and hospitals participated in a 35-question, cross-sectional online survey to assess clinical routines, workflow processes, and policies relating to ACP. RESULTS: Respondents were 53% physicians, 18% advanced practice nurses, 11% nurses, and 18% other interprofessional team members including administrators, chaplains, social workers, and others. Regarding clinical routines, respondents reported that several interprofessional team members play a role in facilitating ACP (ie, physician, social worker, nurse, others). Most (62%) settings did not have, or did not know of, policies related to ACP documentation. Only 14% of settings had a patient education program. Two-thirds of the respondents said that addressing ACP is a high priority and 85% felt that nonphysicians could have ACP conversations with appropriate training. The clinical resources needed to improve clinical routines included training for providers and staff, dedicated staff to facilitate ACP, and availability of patient/family educational materials. CONCLUSION: Although interprofessional health care team members consider ACP a priority and several team members may be involved, clinical settings lack systematic clinical routines to support ACP. Patient educational materials, interprofessional team training, and policies to support ACP clinical workflows that do not rely solely on physicians could improve ACP across diverse clinical settings.
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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 | 0.006 | 0.016 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".