USING SIMULATION TO ASSESS THE FIDELITY OF ADVANCE CARE PLANNING IN THE CONTEXT OF A PRAGMATIC TRIAL
Bibliographic record
Abstract
Abstract There is growing recognition of the importance of and challenges to maintaining fidelity in pragmatic randomized clinical trials. Simulations using standardized patients are a high-fidelity, low-stake, non-threatening opportunity to evaluate knowledge, skills, and competencies associated with high-quality healthcare delivery. We created standardized patient scenarios grounded in the Respecting Choices First Steps™ Advance Care Planning (ACP) curriculum to assess embedded trial ACP facilitators. Scenarios included simulations representing one-on-one encounters with a patient, and with a patient-family dyad. A standardized encounter observation checklist was used to assess and score relevant skills and behaviors including encounter set-up, ACP topics, and general communication. Each item was scored on a scale from not-done (0) to effective (2) with lower scores indicating lower fidelity. Six facilitators with varied backgrounds (social work, nursing, lay persons) each completed the two simulation scenarios. Group average domain scores across all six facilitators were moderately high. ACP Setup scoring averaged 75.5%; ACP Topics were 72.0%; and Communication were 77.4%. The lowest group scoring was observed in the coverage of ACP Topics (72%). The highest group average was observed in Communication skills at 84.9%. Lower individual scores were observed across all domains for staff who were newly hired at the time of the simulation exercise. Simulation using standardized patients and caregivers allowed investigators to monitor the fidelity of ACP communication to the trial design and provided targeted opportunities for improvement that were not readily available through usual care.
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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 | 0.111 | 0.317 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".