Student Perceptions to Teaching Undergraduate Anatomy in Health Sciences
Bibliographic record
Abstract
Anatomy and physiology teaching has undergone significant changes to keep up with advances in technology and to cater for a wide array of student specific learning approaches. This paper examines perceptions towards a variety of teaching instruments, techniques, and innovations used in the delivery and teaching of anatomy and physiology for health science students, and asks whether active learning through more progressive methods of teaching is beneficial for students across health science disciplines. In total, 138 health science students consisting of 32 biomedical science students, 52 exercise and sports science students, and 54 health and physical education students completed the retrospective study. Biomedical science students were least receptive to progressive teaching modalities, preferring anatomical dissections to laboratory workbooks ( p <0.05) and body painting ( p <0.05). In comparison, students from health and sport related degrees responded significantly better to anatomical models and laboratory workbooks than anatomical dissections ( p <0.001). While gender differences were subtle, males responded positively to online multiple-choice question resources (p < 0.05) in comparison to females. Following a multimodal delivery of anatomy and physiology, students from all cohorts reported feeling significantly more confident ( p < 0.005) when discussing all material in the course. The results obtained demonstrate differences amongst cohorts, which indicate that student perceptions to learning anatomy and physiology are dependent on individual course expectations. Moreover, these results support “hands on” practical teaching, and the use of a variety of teaching tools to foster learning and enjoyment of anatomy and physiology in health sciences.
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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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".