“iBIM” — Internet-based interactive modules: an easy and interesting learning tool for general surgery residents
Bibliographic record
Abstract
BACKGROUND: The increased use of information technology supports a resident- centred educational approach that promotes autonomy, flexibility and time management and helps residents to assess their competence, promoting self-awareness. We established a web-based e-learning tool to introduce general surgery residents to bariatric surgery and evaluate them to determine the most appropriate implementation strategy for Internet-based interactive modules (iBIM) in surgical teaching. METHODS: Usernames and passwords were assigned to general surgery residents at the University of Alberta. They were directed to the Obesity101 website and prompted to complete a multiple-choice precourse test. Afterwards, they were able to access the interactive modules. Residents could review the course material as often as they wanted before completing a multiple-choice postcourse test and exit survey. We used paired t tests to assess the difference between pre- and postcourse scores. RESULTS: Out of 34 residents who agreed to participate in the project, 12 completed the project (35.3%). For these 12 residents, the precourse mean score was 50 ± 17.3 and the postcourse mean score was 67 ± 14 (p = 0.020). CONCLUSION: Most residents who participated in this study recommended using the iBIMs as a study tool for bariatric surgery. Course evaluation scores suggest this novel approach was successful in transferring knowledge to surgical trainees. Further development of this tool and assessment of implementation strategies will determine how iBIM in bariatric surgery may be integrated into the curriculum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".