Undergraduate Anatomy Education: Improving Course Assessment to Reduce Student Stress
Bibliographic record
Abstract
Anatomy is the foundational and most significant discipline in medical education. It is a field of biology that deals with the structure and organization of living organisms. Along with learning academic skills in the anatomy courses, medical students have the chance to develop their leadership, teamwork, and communication abilities. The majority of medical students experience stress and may even have psychiatric illnesses as a result of their unique medical education. The primary goal of this paper is to contrast the variations in systematic human anatomy education’s finer points between China and Canada. Additionally, medical students will be asked to respond to a questionnaire to assess their satisfaction with the educational specifics, stress levels, and recommendations for improvement at their respective institutions. The questionnaire responses were subjected to a meta-analysis in this article using STATA in order to better understand the relationship between student stress and course specifics. The paper concludes by discussing ways to enhance inventive anatomy instruction so that students are more interested in anatomy and experience less stress while learning. These include creating online teaching tools like simulation software or 3D models, broadening the range of tasks, adjusting assessment methods, and extending the duration of anatomy.
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 | 0.026 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".