Medical education for millennials: How anatomists are doing it right
Bibliographic record
Abstract
Millennial students born between 1980 and 1999 are currently the most prevalent generation in medical schools. Understanding this generation of inspiring yet challenging learners is key to satisfying instructional interaction. Effective strategies for teaching millennial learners can be summarized with 5 R's: ensuring a relaxed learning environment, building rapport with learners, highlighting the relevance and rationale of learning objectives and assessments, and implementing research-based educational methods. These strategies are exemplified by anatomists who relate (through platforms that encourage team-based learning in a relaxed environment), resonate (by highlighting the relevance and rationale of basic science learning objectives and feedback strategies), and innovate (by adopting cutting edge, research-proven technologies) within their curricula. Anatomists lead the way in effectively engaging, teaching and evaluating Millennial medical students in the 21st century. Broad application of these principles by other medical educators can further enhance Millennial education. Clin. Anat., 2018. © 2018 Wiley Periodicals, Inc.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".