An analysis of anatomy education before and during Covid‐19: August–December 2020
Bibliographic record
Abstract
Coronavirus disease-2019 (Covid-19) disrupted the in-person teaching format of anatomy. To study changes in gross anatomy education that occurred during August-December, 2020 compared to before the pandemic, an online survey was distributed to anatomy educators. The 191 responses received were analyzed in total and by academic program, geographic region, and institution type. Cadaver use decreased overall (before: 74.1 ± 34.1%, during: 50.3 ± 43.0%, P < 0.0001), as well as across allopathic and osteopathic medicine, therapy, undergraduate, and veterinary programs (P < 0.05), but remained unchanged for other programs (P > 0.05). Cadaver use decreased internationally and in the US (P < 0.0001), at public and private (P < 0.0001) institutions, and among allopathic medical programs in Northeastern, Central, and Southern (P < 0.05), but not Western, US geographical regions. Laboratories during Covid-19 were delivered through synchronous (59%), asynchronous (4%), or mixed (37%) formats (P < 0.0001) and utilized digital resources (47%), dissection (32%), and/or prosection (21%) (P < 0.0001). The practical laboratory examination persisted during Covid-19 (P = 0.419); however, the setting and materials shifted to computer-based (P < 0.0001) and image-based (P < 0.0001), respectively. In-person lecture decreased during Covid-19 (before: 88%, during: 24%, P = 0.003). When anatomy digital resources were categorized, dissection media, interactive software, and open-access content increased (P ≤ 0.008), with specific increases in BlueLink, Acland's Videos, and Complete Anatomy (P < 0.05). This study provided evidence of how gross anatomy educators continued to adapt their courses past the early stages of the pandemic.
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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".