Primary care clinicians’ confidence, willingness participation and perceptions of roles in advance care planning discussions with patients: a multi-site survey
Bibliographic record
Abstract
BACKGROUND: People who engage in advance care planning (ACP) are more likely to receive health care that is concordant with their goals at the end of life. Little discussion of ACP occurs in primary care. OBJECTIVE: The objective of this study was to describe primary care clinicians' perspectives on having ACP conversations with their patients. METHODS: We conducted a survey of family physicians and non-physician clinicians in primary care in 2014-2015. We compared family physicians and non-physician clinicians on willingness, confidence, participation and acceptability for other clinicians to engage in six aspects of ACP (initiating, exchanging information, decision coaching, finalizing plans, helping communicate plans with family members and other health professionals) on scales from 0 = not at all/extremely unacceptable to 6 = very/all the time/extremely acceptable. RESULTS: The response rate was 72% (n = 117) among family physicians and 69% (n = 64) among non-physician clinicians. Mean ratings (standard deviation [SD]) of willingness were high (4.5 [1.4] to 5.0 [1.2] for physicians; 3.4 [1.8] to 4.6 [1.6] non-physician clinicians). There was little participation (mean ratings 2.4 [1.7] to 2.7 [1.6] for physicians, 1.0 [1.5] to 1.4 [1.7] for non-physician clinicians). Non-physician clinicians rated confidence statistically significantly lower than physicians for all ACP aspects. Acceptability for non-physician clinician involvement was high in both groups (mean acceptability ratings greater than 4). CONCLUSION: Current engagement of primary care clinicians in ACP is low. Given the high willingness and acceptability for non-physician clinician involvement, increasing the capacity of non-physician clinicians could enable uptake 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 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".