Behaviors in Advance Care Planning and ACtions Survey (BACPACS): development and validation part 1
Bibliographic record
Abstract
BACKGROUND: Although advance care planning (ACP) is fairly well understood, significant barriers to patient participation remain. As a result, tools to assess patient behaviour are required. The objective of this study was to improve the measurement of patient engagement in ACP by detecting existing survey design issues and establishing content and response process validity for a new survey entitled Behaviours in Advance Care Planning and ACtions Survey (BACPACS). METHODS: We based our new tool on that of an existing ACP engagement survey. Initial item reduction was carried out using behavior change theories by content and design experts to help reduce response burden and clarify questions. Thirty-two patients with chronic diseases (cancer, heart failure or renal failure) were recruited for the think aloud cognitive interviewing with the new, shortened survey evaluating patient engagement with ACP. Of these, n = 27 had data eligible for analysis (n = 8 in round 1 and n = 19 in rounds 2 and 3). Interviews were audio-recorded and analyzed using the constant comparison method. Three reviewers independently listened to the interviews, summarized findings and discussed discrepancies until consensus was achieved. RESULTS: Item reduction from key content expert review and conversation analysis helped decrease number of items from 116 in the original ACP Engagement Survey to 24-38 in the new BACPACS depending on branching of responses. For the think aloud study, three rounds of interviews were needed until saturation for patient clarity was achieved. The understanding of ACP as a construct, survey response options, instructions and terminology pertaining to patient engagement in ACP warranted further clarification. CONCLUSIONS: Conversation analysis, content expert review and think aloud cognitive interviewing were useful in refining the new survey instrument entitled BACPACS. We found evidence for both content and response process validity for this new tool.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".