108: iLEARN-Peds: Using Case-Based elearning to Optimize Pediatric Resident Education and Support Program Expansion
Bibliographic record
Abstract
Exposure to Pediatrics is a unique and important part of many residency programs. However, increased numbers of residents due to the expansion of positions, short clinical rotations, and seasonal variability of pediatric cases can result in reduced clinical exposure, uneven clinical experiences, and unmet learning needs. iLEARN-Peds was therefore developed as a creative solution to ensure achievement of Pediatric competencies. iLEARN-Peds is a series of eLearning modules designed to complement clinical experiences taking an experiential, case-based approach. Module cases were selected by analyzing the pediatric learning objectives from all postgraduate programs whose residents rotate through Pediatrics. The most prevalent topics across programs were identified and cross-referenced with hospital visit data to determine the likelihood of residents' exposure to the cases. Seasonal patterns of cases were then noted. Using these data, an expert panel selected the cases to be addressed by the modules. Five modules were developed, piloted with residents for one year during their pediatric emergency rotations, and evaluated using a mixed-methods approach. Residents completed a pre-post knowledge test (10 items/module) and a satisfaction survey for each module, and participated in an expert-led one-hour face-to-face group tutorial that included a reflective exercise. Of the 237 residents who completed the pediatric emergency rotation, 199 completed the learning modules and 120 also completed the pre and post tests and satisfaction surveys. Results revealed high module satisfaction rates and a significant (P<0.001) improvement in knowledge following module completion. Findings from the tutorials suggest learners have been applying their new knowledge in the clinical setting. A series of case-based experiential eLearning modules can complement clinical experiences in Pediatrics. Using the five pilot cases to refine our process allowed for the creation of an additional 11 modules. iLEARN-Peds is now a mandatory component of the family medicine residency program and the pediatric emergency rotation. The platforms and processes developed through this project now serve as the Faculty of Medicine's foundation for eLearning. Plans are underway to expand access to the modules and allow more comprehensive user tracking in keeping with the strategic direction of competency-based medical education.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".