Inflammatory Bowel Disease Training During Adult Gastroenterology Residency: A National Survey of Canadian Program Directors and Trainees
Bibliographic record
Abstract
Abstract Background Clinical training in inflammatory bowel disease (IBD) is a mandated component of adult gastroenterology fellowship. This study aims to assess methods of instruction in IBD and identify priorities and gaps in IBD clinical training among residents and program directors (PDs). Methods Using both an online and in-person platform, we administered a 15-question PD survey and 19-question trainee survey that assessed the methods of IBD teaching and trainee perceptions of knowledge transfer of 22 IBD topics. The survey was previously developed and administered to US gastroenterology trainees and PDs. Results Surveys were completed by 9 of 14 (62.3%) PDs and 44 of 62 (71%) trainees. Both trainee years were equally represented (22 residents in each year of training). All respondents were based at university teaching hospitals with full-time IBD faculty on staff. Dedicated IBD rotations were not offered by more than half of training programs, and IBD exposure was most commonly encountered during inpatient rotations. Overall, only 14 (31.2%) trainees were fully satisfied with the level of IBD exposure during their training. Thirty-six (81.8%) trainees reported being comfortable with inpatient IBD management, whereas only 23 (52.3%) trainees reported being comfortable with outpatient IBD management. There was strong concordance between the proportion of PDs ranking an IBD topic as essential and trainee comfort in that area (Pearson’s rho 0.59; P=0.004). Fewer than half of trainees reported comfort in 11 of 22 (50%) proposed IBD topics. Identified areas of deficiency included phenotypic and endoscopic classification of IBD, inpatient management of severe active IBD, perianal disease management, monitoring biologic therapy and extra-intestinal manifestations of IBD. Conclusions Only one-third of Canadian gastroenterology trainees are fully satisfied with the level of IBD exposure under the current training model. Furthermore, several IBD topics appear to be inadequately covered during training. Our findings, which are similar to previously published US data, highlight the need for additional focus on IBD during gastroenterology residency.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".