Computing for Medicine: Can We Prepare Medical Students for the Future?
Bibliographic record
Abstract
PROBLEM: Technology can transform health care; future physicians need to keep pace to ensure optimal patient care. Because future doctors are poorly prepared in computer literacy, the authors designed a computer programming certificate course. This Innovation Report describes the course and findings from a qualitative study to understand the ways it prepares medical students to use computing science and technology in medicine. APPROACH: The 14-month Computing for Medicine certificate course (C4M, offered beginning in February 2016), University of Toronto, is comprised of hands-on workshops to introduce programming accompanied by homework exercises, seminars by computer science experts on the application of programming to medicine, and coding projects. Using purposive and maximal variation sampling, 17 students who completed the course were interviewed from April-May 2017. Thematic analysis was performed using an iterative constant comparison approach. OUTCOMES: Participants praised the C4M as an opportunity to achieve computer literacy-including language, syntax, and fundamental computational ideas (and their application to medicine)-and acquire or strengthen algorithmic and logical thinking skills for approaching problems. They highlighted that the course illustrated linkages between computer science and medicine. Participants acknowledged a sometimes-existent chasm between producers and users of technology in medicine, recommending two-way communication between the disciplines when developing technology for use in medicine. NEXT STEPS: We recommend that medical schools consider computer literacy an essential skill to foster future collaborative computing partnerships for improved technology use by physicians and optimal patient care. We encourage further evaluation of future iterations of the C4M and similar courses.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".