Medical education for millennials: How anatomists are doing it right
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it