Shoestring Budgets, Band-Aids, and Team Work: Challenges and Motivators in the Development of a Web-Based Resource for Undergraduate Clinical Skills Teaching
Bibliographic record
Abstract
BACKGROUND: Learning how to conduct a medical interview and perform a physical examination is fundamental to the practice of medicine; however, when this project began, the methods used to teach these skills to medical students at the University of Toronto (U of T) had not changed significantly since the early 1990s despite increasing outpatient care, shorter hospital stays, and heavy preceptor workloads. In response, a Web-based clinical skills resource was developed for the first-year undergraduate medical course-The Art and Science of Clinical Medicine I (ASCM I). OBJECTIVES: This paper examines our experiences with the development of the ASCM I website and details the challenges and motivators inherent in the production of a Web-based, multimedia medical education tool at a large Canadian medical school. METHODS: Interviews and a focus group were conducted with the development team to discover the factors that positively and negatively affected the development process. RESULTS: Motivating factors included team attributes such as strong leadership and judicious use of medical students and faculty volunteers as developers. Other motivators included a growing lack of instructional equivalency across diverse clinical teaching sites and financial and resource support by the Faculty of Medicine. Barriers to development included an administrative environment that did not yet fully incorporate information technology into its teaching vision and framework, a lack of academic incentive for faculty participation, and inadequate technical support, space, and equipment. CONCLUSIONS: The success of electronic educational resources such as the ASCM I website has caused a significant cultural shift within the Faculty of Medicine, resulting in the provision of more space, resources, and support for IT endeavours in the undergraduate medical 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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".