86 Virtual MacPeds: Leveling up to the new virtual reality of medical education
Bibliographic record
Abstract
Abstract Primary Subject area Medical Education Background The COVID-19 pandemic and physical distancing measures limited in-person learning experiences for resident postgraduate learners through mandated social distancing measures. Our training program responded by creating online synchronous and asynchronous learning opportunities to supplement learning and replace lost experiences. Virtual MacPeds is an online curriculum created using Kern’s six-step approach to curriculum development to supplement resident learning during COVID-19. The curriculum included online lectures, a live teaching calendar that includes links to sessions across rotations and subspecialties, and a resource rolodex with links to online learning materials. Objectives The purpose of this study is to evaluate the components of the Virtual MacPeds curriculum that were most useful to residents. Design/Methods Virtual MacPeds was piloted from March 2020 to June 2020 to 51 core pediatric residents in PGY1-4. The Kirkpatrick Model for learning evaluation was used to assess resident reaction to the implementation of the curriculum. A voluntary online survey was emailed to residents with the opportunity to respond from June to July 2020. Descriptive statistics were used to assess learner engagement and perception of the curriculum. Results Resident response rate was 38.2% (n=20, PGY1 =8, PGY2 =6, PGY3=5, PGY4=1). 95% of respondents had reduced in-person teaching sessions during COVID-19 and 80% had impacted clinical rotations (self-isolation, virtual care, patient volumes). Prior to Virtual MacPeds, 65% used online educational resources. 95% used the curriculum, all of whom found it helpful in fulfilling Royal College learning objectives. 85% of participants attended the online lectures - those who did not attend noted schedule conflict. 100% would use Virtual MacPeds in the future. Participants noted that Virtual MacPeds should include a live teaching schedule (100%), online lectures (84.2%), self-study modules (73.7%), resource rolodex (52.6%) with suggestions for recorded lectures (89.5%) and simulations (57.9%). Conclusion Virtual MacPeds is an acceptable and useful supplement to resident learning during COVID-19. Useful elements of the curriculum include online lectures, a live teaching schedule, resource rolodex and self-study modules.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".