A Curriculum Review of Programming Courses in a Master of Biostatistics Program
Bibliographic record
Abstract
Computing is a core competency for collaborative biostatisticians, who work on interdisciplinary scientific teams in medicine and publich health. However, computing is a broad field that encompases many underlying pedagogical constructs and subspeciality topics, not all of which are relevant for practicing biostatisticians. Furthermore, the ubiquitous nature of computing in biostatistics and the variation in computing education required to support students’ interests in biostatistics subspecialities presents a challenge for curriculum design. Specifically: where and how to integrate training in computing into biostatistics educational programs? We discuss here our answer to these questions as it relates to a 2-year, full-time, intensive master’s degree program in biostatistics. Specifically, we define and rationalize the core pedagogical contstruct that guided our curriculum deisgn—computational thinking—and then discuss our two-fold approach to training biostatistics master’s students in computing: creation of a 2-course series of targeted training in computing, and embedding training in computing throughout other courses in the curriculum. A detailed description of the course design is included along with a description of our instructional methods, including how we evaluate student performance.
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.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".