Shifts in Digital Resources Usage for Gross Anatomy Education During Covid‐19
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVE: Covid‐19 has led to sudden changes to gross anatomy education when traditional dissection‐based laboratories had to shift towards virtual modalities due to physical distancing and remote learning requirements. The purpose of this study was to determine how the use of digital teaching resources in gross anatomy education changed from before to during Covid‐19. MATERIAL/METHODS: Data were obtained from an IRB‐approved survey distributed to professional associations and listservs targeting anatomy educators from June to November 2020. Respondents were asked to select the digital resources they used before and during Covid‐19. Data were analyzed during the early and latter parts of the pandemic as May‐August (T1) and August‐December (T2), as well as overall (T3). T2 data were classified into five categories: 2D illustrations, dissection media, interactive software, in‐house, and open access. Total usage for each timepoint, the proportions of digital resources, and the 5 categories before and during Covid‐19 were compared using McNemar's test with alpha<5%. Data are presented as percent increase (+value) or decrease (‐value). RESULTS: 60 and 208 responses were received for T1 and T2, respectively. The total number of digital resources used for anatomy education increased from before to during COVID‐19 as seen in the data analysis from T1 (+47%), T2 (+41%), and T3 (+43%) (P≤0.003). In T1, the use of BlueLink (+122%) and Complete Anatomy (+140%) software increased (P<0.04), while Acland's Anatomy (+68%), Anatomy.TV (+1300%), Complete Anatomy (+89%), and BlueLink (+148%) increased in T2 (P≤0.03). During T3, the usage of Acland's Anatomy (+60%), Anatomy.TV (+750%), Complete Anatomy (+102%), BlueLink (+143%), and VisibleBody (+100%) increased (P≤0.03). All other digital resources did not change (P>0.05). When data for T2 were categorized, dissection media (+44%), interactive software (+87%), and open‐access (+100%) content increased (P≤0.008), while 2D illustrations (‐3%) and in‐house content (‐23%) decreased (P>0.05). CONCLUSIONS: This study demonstrates sustained increases in digital resource usage for gross anatomy education during Covid‐19. This was particularly pronounced for interactive software, open access resources and dissection media that allowed educators to mimic features of a dissection lab. SIGNIFICANCE: These rapid shifts in both commercial and free digital resources are likely to drive innovation in anatomy education for years to come. It remains unknown if the current findings are transient Covid‐19‐related changes or if they will persist long‐term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".