The Efficacy of Digital Media in Teaching Anatomy During the COVID‐19 Pandemic
Bibliographic record
Abstract
Introduction The traditional method of teaching anatomy has been through live in person lectures and dissection labs as well as textbooks and other course materials. However, during the COVID‐19 pandemic, these resources were no longer available due to health and safety protocols. With the advancement of technology, it was possible to create anatomy walkthrough videos for the students to supplement the traditional methods of teaching anatomy. Goal To investigate the effectiveness of a video based guide to learning gross anatomy of the boney pelvis and the viscera. Methods Two videos were created covering the boney pelvis and the viscera for the first and second year medical students. The videos were presented by an anatomy professor and filmed and edited by Medical Students. The video covers the curriculum outline, labels relevant parts of the anatomy, and provides extra information relating to form and function. The videos included a quiz at the end for students to test their knowledge. The videos were made available online on Youtube to an international audience, along with a feedback survey. Results Feedback was collected on the boney pelvis and the viscera videos. The respondents watched the videos for the purpose of preparing for anatomy labs and dissections, preparing for anatomy lectures and preparing for examinations. The majority of respondents agreed or strongly agreed that the videos presented assisted them in fulfilling their indicated purpose (89.1%), that a combination of video and written materials should be used for effective learning of anatomy (95.7%), and that they would like to see videos for other anatomical regions (95.7%). Conclusion In summary, digital media in the form of video‐based anatomy guides may be useful in instances when in person teaching is not possible and may be a useful tool to be incorporated into the educational curriculum in combination with traditional teaching methods. Future goals include implementing student feedback and reaching a broader audience.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".