UNDERSTANDING THE CHALLENGES IN PEDIATRIC ENDOSCOPY TRAINING: PATHWAYS TOWARDS COMPETENCE
Bibliographic record
Abstract
Background: Effective endoscopy in pediatric gastroenterology requires seamless integration of technical skill, clinical judgement and clear communication. Considerable variation exists across training programs, raising concerns about the reliance on procedural volume as a marker of competence. This thesis aims to explore strategies for endoscopic training, identify areas that are challenging in pediatrics, and determine existing gaps in pediatric endoscopy education. Methods: This sandwich thesis comprises of two distinct studies. The first is a scoping review of the literature published over the past decade (2014-2024) examining educational interventions in endoscopy training within gastroenterology and general surgery. Second, a qualitative descriptive study involving semi-structured interviews with pediatric gastroenterology faculty and trainees at training centers across Canada. The interviews explored essential skills for pediatric endoscopy, experiences with simulation and perceived gaps in current training practices. Results: The scoping review (n=179) revealed a wide range of educational interventions including didactic sessions, simulation-based training and hands-on procedural instruction. Additionally, it highlighted the inconsistent use of several assessment tools, underscoring a lack of consensus for both training and assessment of endoscopy which is amplified in pediatrics. The findings from the qualitative study identified four key themes that elaborated on the these findings, including the critical role of cognitive and integrative skills, the progressive complexity of challenges within training, and the need for a more structured approach to both training and assessment. Conclusions: By exploring the breadth of the evidence from the scoping review and a the depth of qualitative insights, this thesis ascertains the current practices and gaps within endoscopy training. Recommendations include developing a structured endoscopy training program incorporating regular and constructive feedback, dedicated teaching sessions covering both fundamental and advanced concepts, guidance on troubleshooting and management of complications for complex procedures, and opportunities for longitudinal simulation practice.
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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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".