Computer Programming: Should Medical Students Be Learning It?
Bibliographic record
Abstract
BACKGROUND: The ability to construct simple computer programs (coding) is being progressively recognized as a life skill. Coding is now being taught to primary-school children worldwide, but current medical students usually lack coding skills, and current measures of computer literacy for medical students focus on the use of software and internet safety. There is a need to train a cohort of doctors who can both practice medicine and engage in the development of useful, innovative technologies to increase efficiency and adapt to the modern medical world. OBJECTIVE: The aim of the study was to address the following questions: (1) is it possible to teach undergraduate medical students the basics of computer coding in a 2-day course? (2) how do students perceive the value of learning computer coding at medical school? and (3) do students see computer coding as an important skill for future doctors? METHODS: We developed a short coding course to teach self-selected cohorts of medical students basic coding. The course included a 2-day introduction on writing software, discussion of computational thinking, and how to discuss projects with mainstream computer scientists, and it was followed on by a 3-week period of self-study during which students completed a project. We explored in focus groups (FGs) whether students thought that coding has a place in the undergraduate medical curriculum. RESULTS: Our results demonstrate that medical students who were complete novices at coding could be taught enough to be able to create simple, usable clinical programs with 2 days of intensive teaching. In addition, 6 major themes emerged from the FGs: (1) making sense of coding, (2) developing the students' skill set, (3) the value of coding in medicine, research, and business, (4) role of teaching coding in medical schools, (5) the concept of an enjoyable challenge, and (6) comments on the course design. CONCLUSIONS: Medical students can acquire usable coding skills in a weekend course. They valued the teaching and identified that, as well as gaining coding skills, they had acquired an understanding of its potential both for their own projects and in health care delivery and research. They considered that coding skills teaching should be offered as an optional part of the 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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".