The EvMed Assessment
Bibliographic record
Abstract
Background and objectives: Universities throughout the USA increasingly offer undergraduate courses in evolutionary medicine (EvMed), which creates a need for pedagogical resources. Several resources offer course content (e.g. textbooks) and a previous study identified EvMed core principles to help instructors set learning goals. However, assessment tools are not yet available. In this study, we address this need by developing an assessment that measures students' ability to apply EvMed core principles to various health-related scenarios. Methodology: consists of questions containing a short description of a health-related scenario followed by several likely/unlikely items. We evaluated the assessment's validity and reliability using a variety of qualitative (expert reviews and student interviews) and quantitative (Cronbach's α and classical test theory) methods. We iteratively revised the assessment through several rounds of validation. We then administered the assessment to undergraduates in EvMed and Evolution courses at multiple institutions. Results: consists of six core questions containing 25 items, and five supplemental questions containing 20 items. Conclusions and implications: is a pedagogical tool supported by a wide range of validation evidence. Instructors can use it as a pre/post measure of student learning in an EvMed course to inform curriculum revision, or as a test bank to draw upon when developing in-class assessments, quizzes or exams.
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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".