A233 IMPLEMENTING RESOURCE STEWARDSHIP INTO UNDERGRADUATE MEDIAL EDUCATION: CHOOSING WISELY CANADA
Bibliographic record
Abstract
It is estimated that up to 30% of medical services in Canada are potentially unnecessary, not supported by evidence, and may even result in preventable harm. This type of practicing negatively impacts patients and the health care system and ultimately, leads to suboptimal quality of care. In order to promote resource stewardship (RS) and improve patients’ health, Choosing Wisely Canada (CWC) was launched in 2014. One CWC initiative involved the development of specialty-specific lists of recommendations, and a gastroenterology list has been endorsed by the Canadian Association of Gastroenterology. Studies suggest that medical education strongly impacts resource utilization in future practice; therefore it is imperative that RS efforts be implemented early in medical training. Using the recommendations of CWC we set out to incorporate RS into the undergraduate medical education (UME) curriculum during the gastrointestinal (GI) course. We implemented this change in an iterative manner to enable us to adapt to feedback. Using a Plan-Do-Study-Act cycle CWC was introduced into the GI course at the University of Calgary Cumming School of Medicine in an iterative manner. In the first iteration, CWC was incorporated by adding relevant recommendations into the case-based small group sessions’ learning objectives and content. Qualitative analysis was performed on the narrative data collected via course end surveys from the first iteration of the curriculum. From this analysis, themes were identified and used to influence future iterations of the RS curriculum. The student post-course survey was completed by 143 students. Sixty percent of students reported the inclusion of CWC improved their ability to develop a management plan. Content analysis identified that the students found RS valuable. However, students found inconsistencies in the teaching of RS between seminars, exams and small group sessions. Many students felt that not enough consistent emphasis was placed on RS and the concept was not clear to them. Lastly, many students felt they were too early into their medical training for them to fully grasp the concept of resource restraint while simultaneously tasked with learning thoroughness of care. RS in UME is felt by students to be an important concept. However, content analysis identified numerous areas for improvement. In the next iteration of the GI course, we added an introductory lecture regarding RS to draw attention to the concept. To further address the inconsistencies, we expanded CWC content into large-group lectures and piloted associated exam questions. Next steps to this study include evaluating feedback from these changes, incorporating new additional GI content recently added to the CWC list of recommendations, and obtaining faculty feedback to help inform additional changes for the subsequent iteration. 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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".