A scoping review of teaching approaches and learning objectives for anatomical variation in gross anatomy courses across degree programs
Bibliographic record
Abstract
Gross anatomy is often taught and assessed based on a "standard" view of the human body, limiting students' exposure to normal variation, creating a potential curricular gap in the training of future anatomists and health care professionals. This scoping review explores learning objectives and teaching approaches associated with anatomical variation in undergraduate, graduate, and professional courses. Six primary databases and gray literature sources were searched. Records that described either implicit or explicit approaches for teaching anatomical variation in gross anatomy courses at the undergraduate, graduate, or professional level were included. Source selection, data extraction, and analysis followed the JBI Manual for Evidence Synthesis. Human donor dissection was the most common approach for implicitly teaching anatomical variation (35.7%, 56/157). Only a few records (25.5%, 40/157) reported examples of explicit teaching approaches, such as student recordings of variation, exposure to multiple donors, and independent research. Reported learning objectives primarily involved awareness, appreciation, and knowledge of anatomical variation. Other learning objectives included the recognition of human diversity, professionalism, collaboration, knowledge through anatomical variation, and navigating anatomical variation. Objectives were mapped to Bloom's Taxonomy, showing that explicit approaches were associated with higher-order learning. This review highlights a gap in the literature regarding the explicit teaching of anatomical variation despite the benefits of explicit approaches for enabling higher-order levels of learning across multiple domains. Recognizing anatomical variation is essential for health care professionals, and this review suggests a need for intentional instructional strategies to better support students in navigating anatomical diversity in educational and clinical contexts.
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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 | 0.030 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.031 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".