What motivates medical students to learn anatomy?
Bibliographic record
Abstract
Independent e‐learning modules (IL's) using sound pedagogy and cognitive learning theories are often used to replace the lost lecture or lab hours once given to anatomy. This change should reflect the documented characteristics of the “net” generation. However, participation in and/or completion of the IL's is low. Our initial hypothesis is that motivation to learn is not tied solely to formal evaluation or comfort with e‐learning. An adjunct to learning anatomy through IL's was offered for Years 1 and 2 medical students. The students organized a surgical oriented anatomy club (SOA) to help them understand the relevant anatomy in a clinical context. This study compares the motivation of students to participate in anatomy taught through the surgery club with that of IL's. The SOA club arranged for surgeons to demonstrate several common surgical procedures. After the demonstration students were asked to review the relevant anatomy on cadavers with the assistance of an anatomist and the surgeon. Participation for the events was voluntary and was full beyond capacity within minutes of posting the session information. Participation rates will be compared to that of IL's. In addition, survey data will gauge student motivation for attending the SOA sessions or completing IL modules. Preliminary results suggest more student initiatives designed with clinical relevancy should be considered part of future medical curricula. Grant Funding Source : None
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".