Implementation and evaluation of a simulation curriculum for paediatric residency programs including just-in-time in situ mock codes
Bibliographic record
Abstract
OBJECTIVE: To develop, implement and evaluate a simulation-based acute care curriculum in a paediatric residency program using an integrated and longitudinal approach. DESIGN: Curriculum framework consisting of three modular, year-specific courses and longitudinal just-in-time, in situ mock codes. SETTING: Paediatric residency program at BC Children's Hospital, Vancouver, British Columbia. INTERVENTIONS: The three year-specific courses focused on the critical first 5 min, complex medical management and crisis resource management, respectively. The just-in-time in situ mock codes simulated the acute deterioration of an existing ward patient, prepared the actual multidisciplinary code team, and primed the surrounding crisis support systems. Each curriculum component was evaluated with surveys using a five-point Likert scale. RESULTS: A total of 40 resident surveys were completed after each of the modular courses, and an additional 28 surveys were completed for the overall simulation curriculum. The highest Likert scores were for hands-on skill stations, immersive simulation environment and crisis resource management teaching. Survey results also suggested that just-in-time mock codes were realistic, reinforced learning, and prepared ward teams for patient deterioration. CONCLUSIONS: A simulation-based acute care curriculum was successfully integrated into a paediatric residency program. It provides a model for integrating simulation-based learning into other training programs, as well as a model for any hospital that wishes to improve paediatric resuscitation outcomes using just-in-time in situ mock codes.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".