Computing for Medicine: Can We Prepare Medical Students for the Future?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it