An Analysis of Anatomy Education Before and During Covid‐19: May–August 2020
Bibliographic record
Abstract
Coronavirus disease 2019 (Covid-19) created unparalleled challenges to anatomy education. Gross anatomy education has been particularly impacted given the traditional in-person format of didactic instruction and/or laboratory component(s). To assess the changes in gross anatomy lecture and laboratory instruction, assessment, and teaching resources utilized as a result of Covid-19, a survey was distributed to gross anatomy educators through professional associations and listservs. Of the 67 survey responses received for the May-August 2020 academic period, 84% were from United States (US) institutions, while 16% were internationally based. Respondents indicated that in-person lecture decreased during Covid-19 (before: 76%, during: 8%, P < 0.001) and use of cadaver materials declined (before: 76 ± 33%, during: 34 ± 43%, P < 0.001). The use of cadaver materials in laboratories decreased during Covid-19 across academic programs, stand-alone and integrated anatomy courses, and private and public institutions (P ≤ 0.004). Before Covid-19, cadaveric materials used in laboratories were greater among professional health programs relative to medical and undergraduate programs (P ≤ 0.03) and among stand-alone relative to integrated anatomy courses (P ≤ 0.03). Furthermore, computer-based assessment increased (P < 0.001) and assessment materials changed from cadaveric material to images (P < 0.03) during Covid-19, even though assessment structure was not different (P > 0.05). The use of digital teaching resources increased during Covid-19 (P < 0.001), with reports of increased use of in-house created content, BlueLink, and Complete Anatomy software (P < 0.05). While primarily representing US institutions, this study provided evidence of how anatomy educators adapted their courses, largely through virtual mediums, and modified laboratory protocols during the initial emergence of the Covid-19 pandemic.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".