Measuring Advance Care Planning: Optimizing the Advance Care Planning Engagement Survey. Copyright: Creative Commons License.
Bibliographic record
Abstract
CONTEXT: A validated 82-item Advance Care Planning (ACP) Engagement Survey measures a broad range of behaviors. However, concise surveys are needed. OBJECTIVES: The objective of this study was to validate shorter versions of the survey. METHODS: The survey included 57 process (e.g., readiness) and 25 action items (e.g., discussions). For item reduction, we systematically eliminated questions based on face validity, item nonresponse, redundancy, ceiling effects, and factor analysis. We assessed internal consistency (Cronbach's alpha) and construct validity with cross-sectional correlations and the ability of the progressively shorter survey versions to detect change one week after exposure to an ACP intervention (Pearson correlation coefficients). RESULTS: Five hundred one participants (four Canadian and three US sites) were included in item reduction (mean age 69 years [±10], 41% nonwhite). Because of high correlations between readiness and action items, all action items were removed. Because of high correlations and ceiling effects, two process items were removed. Successive factor analysis then created 55-, 34-, 15-, nine-, and four-item versions; 664 participants (from three US ACP clinical trials) were included in validity analysis (age 65 years [±8], 72% nonwhite, 34% Spanish speaking). Cronbach's alphas were high for all versions (four items 0.84-55 items 0.97). Compared with the original survey, cross-sectional correlations were high (four items 0.85; 55 items 0.97) as were delta correlations (four items 0.68; 55 items 0.93). CONCLUSION: Shorter versions of the ACP Engagement Survey are valid, internally consistent, and able to detect change across a broad range of ACP behaviors for English and Spanish speakers. Shorter ACP surveys can efficiently measure broad ACP behaviors in research and clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".