Exploring Use of Endoscopy Simulation in North American Pediatric Gastroenterology Fellowship Training Programs
Bibliographic record
Abstract
OBJECTIVES: Increasing evidence supports simulation-based training; however, limited data exist regarding its use in pediatric gastroenterology (GI). We explored the use of simulation-based endoscopy training in pediatric GI fellowship programs across North America. METHODS: GI fellowship program directors (PDs) from the United States and Canada were surveyed between August to November 2018. The pretested, electronic survey comprised 3 sections: program demographics; details of current simulation-based training; and PDs' perceptions of endoscopy simulation. Responses were analyzed using descriptive statistics. RESULTS: Forty-three of 71 (61%) PDs responded (6 Canadian, 37 US). Programs were predominantly academic (95%) and enrolled 1.87 ± 1.01 fellows/yr. Twenty-four programs (56%) reported using simulation for endoscopy training, whereas 8 (19%) used simulation for nonprocedural education. Only 2 programs (5%) used endoscopy simulation for assessment. Of those using simulation (n = 24), upper endoscopy and colonoscopy were trained most frequently, and mechanical simulators were used most commonly. Eight programs (33%) required simulation training prior to clinical performance. Although 10 programs (42%) provided protected training time, only 2 (8%) tracked hours. Three programs (13%) reported having an organized curriculum and 6 (25%) train their endoscopic trainers. Cost, time constraints, and lack of a standardized curriculum were perceived as key barriers to integration. Most PDs reported a need for endoscopy simulation to train both technical and nontechnical skills; however, they felt simulation cannot replace clinical experience. CONCLUSION: PDs recognize the potential importance of endoscopy simulation, particularly for novices; however, only 56% report using it. Perceived barriers indicate the need for inexpensive portable simulators and a validated pediatric simulation curriculum to promote uptake.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".