134: Any Place, Any Pace Pediatric Acute Care: An Online Mock Code Curriculum
Bibliographic record
Abstract
It is well-documented that participation in mock codes improves resident confidence and knowledge in pediatric acute care. While real-life critical pediatric events are extremely rare, mismanagement bears serious consequences, rendering a mock code curriculum an essential part of pediatric residency training. Traditional mock codes, however, involve only a small number of learners within a limited time-frame due to competing clinical and academic demands. To determine the impact of an online mock code curriculum on acute care exposure during pediatric residency. In addition, we aim to assess the curriculum's impact on resident perception of acute care knowledge, awareness of crisis resource management principles, confidence in managing critical events, and competency in relevant RCPSC Objectives of Training. A tiered longitudinal curriculum was designed. Mock code scenarios were authored by pediatric residents with PICU mentorship. Content was based on applicable RCPSC Objectives of Training addressed in that month's academic half-day curriculum. Four residents participated in a monthly PICU staff-facilitated interdisciplinary mock code. The authoring resident assisted with the scenario and debrief, thereafter distributing a computer-based module to all residents, comprising a) the scenario in stepwise form, b) targeted RCPSC objectives, and c) key learning points and resources. Over a nine-month period, nine modules have been designed and disseminated to all 50 residents. Thirty-six residents have participated in one or more simulated scenarios which, as surrogates of clinical encounters, represents a five-fold increase in pediatric acute care exposure. A total of 92 RCPSC Medical Expert and 38 intrinsic competency objectives encompassing all CanMeds domains have been met to date. Results of a confidence and satisfaction survey are pending. Given that critical events are relatively infrequent in pediatric patients, computer-based modules that are widely disseminated and easily accessible to pediatric residents can increase exposure to acute care issues in a way that is patient-safe. This innovative mock code curriculum effectively targets multiple RCPSC objectives encompassing all CanMeds roles, and may easily be adapted for other areas of medical expertise and residency programs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".