Confronting the Challenges of Anatomy Education in a Competency-Based Medical Curriculum During Normal and Unprecedented Times (COVID-19 Pandemic): Pedagogical Framework Development and Implementation
Bibliographic record
Abstract
BACKGROUND: Anatomy is considered to be one of the keystones of undergraduate medical education. However, recently, there has been drastic reduction, both in gross anatomy teaching hours and its context. Additionally, a decrease in the number of trained anatomists and an increase in the costs associated with procuring human cadavers have been noted, causing a diminution of cadaveric dissections in anatomy education. OBJECTIVE: To address these challenges, there is an ardent need for a pedagogical framework such that anatomy education can be disseminated through active learning principles, within a fixed time frame, using a small team of anatomists and a small number of cadaveric specimens (for live on-site sessions) as well as collaborative learning principles. The latter is particularly important when anatomy education is delivered through distance learning, as is the case currently during the COVID-19 pandemic. METHODS: Here, we have blueprinted a pedagogical framework blending the instructional design models of Gagne's 9 events of instruction with Peyton's 4-step approach. The framework's applicability was validated through the delivery of anatomical concepts, using an exemplar from the structure-function course Head and Neck during the normal and COVID-19-mandated lockdown periods, employing the archetype of Frey syndrome. Preliminary evaluation of the framework was pursued using student feedback and end-of-course feedback responses. The efficiency of the framework in knowledge transfer was also appraised. RESULTS: The blueprinted instructional plan designed to implement the pedagogical framework was successfully executed in the dissemination of anatomy education, employing a limited number of cadaveric specimens (during normal times) and a social media application (SMA)-integrated "interactome" strategy (during the COVID-19 lockdown). Students' response to the framework was positive. However, reluctance was expressed by a majority of the faculty in adopting the framework for anatomy education. To address this aspect, a strategy has been designed using Mento's 12-step change management model. The long-term benefits for any medical school to adopt the blended pedagogical framework have also been explicated by applying Bourdieu's Theory of Practice. Additionally, through the design of an SMA interactome model, the framework's applicability to the delivery of anatomy education and content during the ongoing COVID-19 pandemic was realized. CONCLUSIONS: In conclusion, the study effectively tackles some of the contemporary key challenges associated with the delivery of anatomy content in medical education during normal and unprecedented times.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".