Preorientation Curriculum: An Approach for Preparing Students with Heterogenous Backgrounds for Training in a Master of Biostatistics Program
Bibliographic record
Abstract
We describe an innovative preorientation curriculum (POC) for a Master of Biostatistics (MB) program. The goal of the POC is to fill critical skills gaps for students entering the MB program from heterogeneous backgrounds so they are prepared to engage in the program’s rigorous, fast-paced training upon arrival. To achieve this goal, we introduce a structured approach to thinking that forms the foundation of a sound mental map of Biostatistics, which will assist students in their subsequent efforts to master the discipline. Based on constructivist principles, the POC covers mathematical and statistical theory, data analysis methods, programming, and statistical practice through a sequence of instruction that encourages reflection, extension, and connection between topics. The POC is modular, self-paced, and offered online via a cloud-hosted interactive learning management system (LMS). Students are required to complete the curriculum prior to the MB program orientation. We describe the rationale, design, features, and initial evaluation of the POC. Finally, to help programs interested in designing similar curricula, we provide a detailed instruction sequence description of one topic.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".