Promoting the Development of Adaptive Expertise: Exploring a Simulation Model for Sharing a Diagnosis of Autism With Parents
Bibliographic record
Abstract
PURPOSE: To explore how a simulation model promoted the development of integrated competencies associated with adaptive expertise in senior health professions trainees as they learned to share a diagnosis of autism with parents. METHOD: A qualitative instrumental case study method was used at the University of Toronto in 2014 to explore what eight developmental pediatrics residents and two clinical psychology interns learned from participating in a simulation model designed to enable trainees to practice sharing a diagnosis of autism with parents. This model incorporated variability (three cases), active experimentation in a safe environment, and feedback from multiple perspectives (peers, faculty, standardized patients, and a parent). Field notes were collected, and semistructured interviews were conducted to explore what participants learned. Constant comparative analysis was used to identify themes iteratively. Team analysis continued until a stable thematic structure was developed and applied to the entire data set. RESULTS: Four themes were identified. Three themes described how participating in the simulation model changed residents' and interns' approaches to sharing a diagnosis of autism with parents from using a structured, scripted framework to share the diagnosis; to being flexible within the structured framework; and, finally, to being attentive and responsive to parents by adapting and creating new approaches for sharing the diagnosis. The fourth theme described how the multiple perspectives in the simulation model prompted learners to develop adaptive approaches. CONCLUSIONS: This simulation model helped residents and interns move beyond use of a structured, scripted communication framework toward development of adaptive expertise.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".