Measuring engagement in advance care planning: a cross-sectional multicentre feasibility study
Bibliographic record
Abstract
OBJECTIVES: To assess the feasibility, acceptability and clinical sensibility of a novel survey, the advance care planning (ACP) Engagement Survey, in various healthcare settings. SETTING: A target sample of 50 patients from each of primary care, hospital, cancer care and dialysis care settings. PARTICIPANTS: A convenience sample of patients without cognitive impairment who could speak and read English was recruited. Patients 50 and older were eligible in primary care; patients 80 and older or 55 and older with clinical markers of advanced chronic disease were recruited in hospital; patients aged 19 and older were recruited in cancer and renal dialysis centres. OUTCOMES: We assessed feasibility, acceptability and clinical sensibility of the ACP Engagement Survey using a 6-point scale. The ACP Engagement Survey measures ACP processes (knowledge, contemplation, self-efficacy and readiness) on 5-point Likert scales and actions (yes/no). RESULTS: 196 patients (38-96 years old, 50.5% women) participated. Mean (±SD) time to administer was 48.8±19.6 min. Mean acceptability scores ranged from 3.2±1.3 in hospital to 4.7±0.9 in primary care, and mean relevance ranged from 3.5±1.0 in hospital to 4.9±0.9 in dialysis centres (p<0.001 for both). The mean process score was 3.1±0.6 and the mean action score was 11.2±5.6 (of a possible 25). CONCLUSIONS: The ACP Engagement Survey demonstrated feasibility and acceptability in outpatient settings but was less feasible and acceptable among hospitalised patients due to length. A shorter version may improve feasibility. Engagement in ACP was low to moderate.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".