Developing and validating an accelerated online Canadian training in data science for the health sciences community
Bibliographic record
Abstract
Background With the rise of digital health, proficiency in data science is becoming increasingly important. University curricula often lack adequate data analysis and programming education, hence the need to develop an accessible training platform. Methods A free, accelerated R programming course was developed for healthcare trainees with no prior programming experience. The first module was composed of seven video capsules over 10 days to teach foundational R programming for clinical research. A pretest and posttest study assessed participants’ skills pretraining, immediately posttraining, and three months later. Participants (students, researchers, professors) were recruited from Montreal's academic healthcare community. A Real-Time Delphi method guided test development and mixed-effects models compared scores. Results Of 102 enrolled participants, 100 were analyzed, which were mostly aged 20–30 (72%) and medical students (92%, 69.6%). Of them, 84% successfully completed the course within 10 days (95% CI [77%—91%]). Mean test scores increased from 4.5/10 pretest (95% CI [4.1–4.8]) to 8.4/10 posttraining (95% CI [8.1–8.7]) ( p < .001; Cohen's d = 2.5), with scores at three months (6.8/10, 95% CI [6.4–7.2]) remaining significantly higher than baseline ( p < .001), despite a slight expected decline. Conclusion This accelerated R programming course effectively improves data science skills in healthcare trainees with no prior knowledge. It addresses key gaps in formal data science education with the potential to enhance independent research and analysis skills in complement to university curricula.
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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".