Learning pediatric emergency medicine over time: A realist evaluation of a longitudinal pediatric emergency medicine clinical experience
Bibliographic record
Abstract
Introduction: Emergency medicine (EM) practitioners must be proficient at caring for patients of all ages, including pediatric patients. Traditionally, EM trainees learn pediatric emergency medicine (PEM) through block rotations. This is problematic due to the seasonal nature of pediatric diseases and infrequent critical events. Spaced repetition learning theory suggests PEM would be better learned through longitudinal rotations. The transition to competency-based medical education (CBME) in Canada is accelerating the need to find novel ways to attain competencies in postgraduate training. At McMaster University, senior EM trainees can choose either traditional PEM blocks or longitudinal rotations. Our objective was to understand how learners experience these different rotations given the transition to CBME in Canada. Methods: Using a realist framework of program evaluation, we conducted semistructured interviews with key stakeholders (trainees, program directors, attending physicians) in EM. The realist framework was used to understand how context interacts with theoretical mechanisms to produce outcomes of interest. Data were analyzed using inductive, conventional content analysis. All investigators coded a subset of transcripts independently and in duplicate to achieve intercoder agreement. Results: = 2). The learning experience exists within an educational and clinical context, which are logistically distinct but inseparable. The longitudinal learning experience appears to improve learning through spaced repetition, which prevents atrophy of skills and knowledge while also benefitting from the offsetting of seasonal variability associated with many pediatric diseases. Improved feedback and entrustment are facilitated through the building of coaching relationships over time. Barriers to the learning experience are related mainly to logistical difficulties associated with resolving longitudinal and blocked learning experiences. Improved relationships with the interprofessional team may provide distinct learning opportunities and improved team functioning. Block rotations were identified as more valuable to junior trainees learning fundamental concepts. Conclusions: Longitudinal learning provides numerous advantages to learning PEM, including increased case variety, spaced repetition of core concepts, and a perception of greater entrustment of the learner through formation of coaching relationships over time. Future projects looking to quantify the differences between longitudinal and block learning to objectively show a difference in skills and knowledge are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 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 teacher head, 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".