Virtual curriculum delivery in the COVID-19 era: the pediatric surgery boot camp v2.0
Bibliographic record
Abstract
PURPOSE: We evaluated the impact of a virtual Pediatric Surgery Bootcamp curriculum on resource utilization, learner engagement, knowledge retention, and stakeholder satisfaction. METHODS: A virtual curriculum was developed around Pediatric Surgery Milestones. GlobalCastMD delivered pre-recorded and live content over a single 10-h day with a concluding social hour. Metrics of learner engagement, faculty interaction, knowledge retention, and satisfaction were collected and analyzed during and after the course. RESULTS: Of 56 PS residencies, 31 registered (55.4%; 8/8 Canadian and 23/48 US; p = 0.006), including 42 learners overall. The virtual BC budget was $15,500 (USD), 54% of the anticipated in-person course. Pre- and post-tests were administered, revealing significant knowledge improvement (48.6% [286/589] vs 66.9% [89/133] p < 0.0002). Learner surveys (n = 14) suggested the virtual BC facilitated fellowship transition (85%) and strengthened peer-group camaraderie (69%), but in-person events were still favored (77%). Program Directors (PD) were surveyed, and respondents (n = 22) also favored in-person events (61%). PDs not registering their learners (n = 7) perceived insufficient value-added and concern for excessive participants. CONCLUSIONS: The virtual bootcamp format reduced overall expenses, interfered less with schedules, achieved more inclusive reach, and facilitated content archiving. Despite these advantages, learners and program directors still favored in-person education. LEVEL OF EVIDENCE: III.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".