A Course in Biology and Communication Skills for Master of Biostatistics Students
Bibliographic record
Abstract
We describe an innovative, semester-long course in biology and communication skills for master’s degree students in biostatistics. The primary goal of the course is to make the connection between biological science and statistics more explicit. The secondary goals are to teach oral and written communication skills in an appropriate context for applied biostatisticians, and to teach a structured approach to thinking that enables students to become lifelong learners in biology, study design, and the application of statistics to biomedical research. Critical evaluation of medical literature is the method used to teach biology and communication. Exercises are constructivist in nature, designed to be hands-on and encourage reflection through writing and oral communication. A single disease area (cancer) provides a motivating example to: 1) introduce students to the most commonly used study designs in medical and public health research, 2) illustrate how study design is used to address questions about human biology and disease, 3) teach basic biological concepts necessary for a successful career in biostatistics, and 4) train students to read and critically evaluate publications in peer-reviewed journals. We describe the design and features of the course, the intended audience, and provide detailed examples for instructors interested in designing 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.033 |
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".