Community-led, peer-facilitated Advance Care Planning workshops prompt increased Advance Care Planning behaviors among public attendees
Bibliographic record
Abstract
Objective: Despite recognized benefits, engagement in Advance Care Planning (ACP) remains low. Research into peer-facilitated, group ACP interventions is limited. This study investigated the acceptability of community-led peer-facilitated ACP workshops for the public and whether these workshops are associated with increased knowledge, motivation and engagement in ACP behaviors. Methods: Peer-facilitators from 9 community organizations were recruited and trained to deliver free ACP workshops to members of the public with an emphasis on conversation. Using a cohort design, workshop acceptability and engagement in ACP behaviors was assessed by surveying public participants at the end of the workshop and 4-6 weeks later. Results: 217 participants returned post-workshop questionnaires, and 69 returned follow-up questionnaires. Over 90% of participants felt they gained knowledge across all 6 learning goals. Every ACP behavior saw a statistically significant increase in participant completion after 4-6 weeks. Almost all participants were glad they attended (94%) and would recommend the workshop to others (95%). Conclusion: This study revealed an association of peer-facilitated ACP workshops and completion of ACP behaviors in public participants. Innovation: This innovative approach supports investment in the spread of community-based, peer-facilitated ACP workshops for the public as important ACP promotion strategies.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".