The Pedagogical Analysis of Digital Media Utilization in the Anatomical Sciences: An Innovative Approach to Teaching the Cadaveric Dissection and Anatomy of the Heart
Bibliographic record
Abstract
Introduction The anatomical sciences have been regarded as the cornerstone of medical education for centuries. Although vital for healthcare students, understanding the human anatomy can be cognitively challenging, especially for beginner learners. Furthermore, lectures and cadaveric dissections as traditional teaching methods can be costly for the corresponding faculty. With advancements in technology, however, multimedia may harbour great potential to assist educators in their tasks and supplement the traditional pedagogy of teaching anatomy. Additionally, learners can have greater accessibility and flexibility to strengthen their knowledge outside of curricular time and learn at their own pace, respectively. Thus, the impact of utilizing multimedia on anatomy education prompts further investigation. Objectives The purposes of this study include understanding the role of a video‐based dissection and anatomy guide and investigating its impact on medical education via learners’ attitudes. Methods A video‐based guide on the cadaveric dissection and anatomy of the heart was filmed at the University of British Columbia, edited using the Camtasia 2020 software, and made available to learners via YouTube, a media platform that is readily accessible to the public. The video features a list of learning objectives, step‐by‐step dissection of the heart, the associated and labeled gross anatomical structures, important and labeled notes related to the structures, dissection techniques for learners, and review questions for viewers to test their knowledge on the content discussed. A feedback survey was included at the end of the video, and responses were collected for a period of three months. Results From October 2020 to January 2021, the cadaveric dissection guide and anatomy video of the heart received 2,036 views, 90 “Likes” and 0 “Dislikes”. During this time, a total number of 65 respondents provided feedback. The majority of respondents are from Canada (90.8%), study Doctor of Medicine (93.8%), and are in their first year of their degree (86.2%). When asked about their purpose(s) for watching this video, respondents indicated that they used the video to prepare for their anatomy lab(s) and dissection(s) (86.2%), anatomy lecture(s) (58.5%), examination(s) (52.3%), for self‐studying (46.2%), and for pleasure (16.9%). Although only 3.2% of the viewers completed the survey, an astonishing number of respondents strongly agreed that the video assisted them in fulfilling their goal(s) (81.5%). On a 5‐Point Likert Scale (“Very Poor” = 1; “Very Good” = 5), the organization and logical flow of the video received the highest score and had the lowest standard deviation (4.80 ± 0.47), while the transitions between contents received the lowest score and had the highest standard deviation (4.45 ± 0.79); the overall quality and rating of the video received a score of 4.71 ± 0.52. Conclusion In summary, the results indicate that multimedia, at least in the format of a video‐based guide, can supplement a learner’s understanding of the dissection and anatomy of the heart. Additionally, video‐directed learning is greatly beneficial in helping them fulfill their purpose(s).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".