A72 INFLAMMATORY BOWEL DISEASE TRAINING DURING ADULT GASTROENTROLOGY FELLOWSHIP: A NATIONAL SURVEY OF CANADIAN PROGRAM DIRECTORS AND TRAINEES
Bibliographic record
Abstract
Clinical training in inflammatory bowel disease (IBD) is a major component of adult gastroenterology fellowship. As Canadian residency programs adopt a competency-by-design (CBD) training model, there is a need to identify core competencies in IBD training. This study aims to identify priorities and deficiencies in IBD clinical training among residents and program directors (PDs). Using an online and paper based platform, we administered a 15-question PD survey and 19-question trainee survey and assessed 22 proposed IBD competencies. The survey was previously developed and administered to US gastroenterology trainees and PDs. Surveys were completed by 9/14 (62.3%) PDs and 44 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. All training programs surveyed offered an additional year of advanced IBD fellowship training. Dedicated IBD rotations were not offered by over half of training programs, and IBD exposure was mostly commonly encountered in inpatient rotations. Overall, only 14 (31.2%) trainees were fully satisfied with the level of IBD exposure during 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 a strong concordance between the proportion of PDs ranking a competency as essential and trainee comfort in that area (Pearson’s rho 0.59; p=0.004). Fewer than half of trainees reported comfort in 11/22 (50%) proposed competencies. 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. Only one-third of Canadian gastroenterology trainees are fully satisfied with the level of IBD exposure under the current training model. Furthermore, several IBD core competencies appear to be inadequately covered during training. Our findings, which parallel previously published US data, highlight the need for additional focus on IBD during gastroenterology fellowship. It is possible that the optimal treatment of patients with IBD may require advanced specialists. None
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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".