Building and Participating in a Simulation: Exploring a Continuing Education Intervention Designed to Foster Reflective Practice Among Experienced Clinicians
Bibliographic record
Abstract
INTRODUCTION: Engaging health professionals in the processes of first building and then participating in simulations has not yet been explored. This qualitative study explored the experience of building and participating in a simulation as an educational intervention with experienced clinicians. METHODS: Pediatric rehabilitation clinicians, along with a patient facilitator and standardized patients, created simulations and subsequently participated in a live simulation. The educational content of the simulation was culturally sensitive communication. We collected participants' perspectives about the process from individual journal entries and focus groups. A thematic analysis of these data sources was conducted. RESULTS: Participants described a process of building and participating in a simulation that provided: 1) a unique opportunity for clinicians to reflect on their current practice; 2) a venue to identify different perspectives through discussion and action in a group; and 3) a safe environment for learning. DISCUSSION: The combined process of building and participating in a simulation stimulated reflection about the clinicians' own abilities in culturally sensitive communication through discussion, practice, and feedback. It provided a safe environment for participants to share their multiple perspectives and to develop new ways of communicating. This type of educational intervention may contribute to the continuing education of experienced clinicians in both academic and community settings.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".