Feasibility of Using Simulation to Evaluate Implementation Fidelity in an Advance Care Planning Pragmatic Trial
Bibliographic record
Abstract
Background and Objectives: Traditional methods of fidelity monitoring are not possible in pragmatic trials in real-world clinical settings. We describe our approach to monitoring and reinforcing the fidelity to ACP conversations for a hard-to-reach subpopulation by using standardized patients in a pragmatic trial. Research Design and Methods: We developed standardized patient scenarios grounded in the Respecting Choices First Steps™ Advance Care Planning curriculum to provide an opportunity to reinforce and assess ACP facilitator competency. Scenarios represented one-on-one encounters. The first case was a standardized patient with cognitive impairment and the second case involved a standardized patient with dementia and their care partner. A previously validated fidelity checklist was used to score skills and behaviors observed during simulations including encounter set-up, ACP topics, and general communication. Simulations involved voice teleconferencing to align primary modality of ACP in the pragmatic trial. Results: Six facilitators completed two standardized patient cases each. Overall fidelity scores were moderately high (78.8% ± 11.7; 63.4 – 95.6) for the case with cognitive impairment and for the case with the patient with dementia and care partner (76.2% ± 13.0; 54.4 – 91.5). Discussion and Implications: Simulation using standardized patients supported fidelity monitoring and provided coachable feedback to support facilitator competency. Our study can help inform future research and training related to advance care planning in older adults living with Alzheimer’s disease and related disorders.
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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 | 0.287 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".