Using Digital Multimedia to Enhance the Learning of Gross Anatomy and Integrating with Clinical Science
Bibliographic record
Abstract
Background With the introduction of reformed curricula in medicine, most schools have reduced the total hours allocated for anatomy teaching and laboratory practical hours. These changes have been a continuous debate and triggered the emergence of innovative teaching and learning strategies in order to maximize students' learning of anatomy in the new context. Interactive modules allow for students to learn at their own pace and engage with their learning through quizzes and media to best supplement learning. The purpose of this project was to develop a module that can help support the learning of second year students in the new curriculum of UBC's Faculty of Medicine. This module was based on the content of a week‐long topic on osteoarthritis (OA) and hand anatomy and focused on the integration of clinical knowledge with the concurrent instruction of pertinent anatomy. Methods An interactive module on hand anatomy and osteoarthritis was developed to reflect curricular content. The module was made available to students on the curriculum website and completion of a voluntary survey of ten subjective questions was encouraged at the end of the module. Results 94% of the participants felt their clinical knowledge of OA was strong or very strong after completion of the module, compared to 44% before completion of the module. Anatomical knowledge of the hand and wrist improved from 5 feeling strong or very strong to 83% of the participants. 88% of the participants felt that the integration of a clinical case of OA with the anatomy was either effective or very effective in assisting learning of the curricular content. 94% of the participants would like to see more modules like this for curricular content. Conclusions The development of an interactive module that explored clinical and anatomical content curricular objectives improved students' subjective knowledge of clinical and anatomical knowledge. Students found it useful for their learning to integrate cases into the core concepts of anatomy and would like to see more modules like this in the future. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".