Impact of COVID‐19 on Pediatric Gastroenterology Fellow Training in North America
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has drastically changed healthcare systems and training around the world. The Training Committee of the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition sought to understand how COVID-19 has affected pediatric gastroenterology fellowship training. METHODS: A 21 question survey was distributed to all 77 pediatric gastroenterology fellowship program directors (PDs) in the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition program director database via email on April 7. Responses collected through April 19, 2020 were analyzed using descriptive statistics. RESULTS: Fifty-one of 77 (66%) PDs from the United States, Canada, and Mexico responded to the survey. Forty-six of 51 (90%) PDs reported that they were under a "stay-at-home" order for a median of 4 weeks at the time of the survey. Two of the 51 (4%) programs had fellows participating in outpatient telehealth before COVID-19 and 39 of 51 (76%) at the time of the survey. Fellows stopped participating in outpatient clinics in 22 of 51 (43%) programs and endoscopy in 26 of 51 (52%) programs. Changes to inpatient care included reduced fellow staffing, limiting who entered patient rooms, and rounding remotely. Fellows in 3 New York programs were deployed to adult medicine units. Didactics were moved to virtual conferences in 47 of 51 (94%) programs, and fellows used various online resources. Clinical research and, disproportionately, bench research were restricted. CONCLUSIONS: This report provides early information of the impact of COVID-19 on pediatric fellowship training. Rapid adoption of telehealth and reduced clinical and research experiences were important changes. Survey information may spur communication and innovation to help educators adapt.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".