How to increase public participation in advance care planning: findings from a World Café to elicit community group perspectives
Bibliographic record
Abstract
BACKGROUND: In 2014, Alberta, Canada broke new ground in having the first provincial healthcare policy and procedure for advance care planning (ACP), the process of communicating and documenting a person's future healthcare preferences. However, to date public participation and awareness of ACP remains limited. The aim of this initiative was to elicit community group perspectives on how to help people learn about and participate in ACP. METHODS: Targeted invitations were sent to over 300 community groups in Alberta (e.g. health/disease, seniors/retirement, social/service, legal, faith-based, funeral planning, financial, and others). Sixty-seven participants from 47 community groups attended a "World Café". Participants moved between tables at fixed time intervals, and in small groups discussed three separate ACP-related questions. Written comments were captured by participants and facilitators. Each comment was coded according to Michie et al.'s Theoretical Domains Framework, and mapped to the Capability, Opportunity and Motivation behavior change system (COM-B) in order to identify candidate intervention strategies. RESULTS: Of 800 written comments, 76% mapped to the Opportunity: Physical COM-B component of behavior, reflecting a need for access to ACP resources. The most common intervention functions identified pertained to Education, Environmental Restructuring, Training, and Enablement. We synthesized the intervention functions and qualitative comments into eight recommendations for engaging people in ACP. These pertain to access to informational resources, group education and facilitation, health system processes, use of stories, marketing, integration into life events, inclusion of business partners, and harmonization of terminology. CONCLUSIONS: There was broad support for the role of community groups in promoting ACP. Eight recommendations for engaging the public in ACP were generated and have been shared with stakeholders.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".