The use of an online anatomy laboratory for allied health education
Bibliographic record
Abstract
In-person cadaveric anatomy laboratories allow for students to learn the intricacies of the human body but also develop skills related to communication, clinical reasoning, and interprofessional collaboration. However, the COVID-19 pandemic caused a shift from in-person course delivery to an online medium. Therefore, the objective of this study was to develop and evaluate the implementation and use of an online anatomy laboratory as a replacement for an in-person laboratory component. An anatomy course for allied heath students (pharmacy and respiratory therapy) that included an in-person cadaveric laboratory was modified for online delivery. The laboratory component utilized cadaveric images presented by the instructor and breakout rooms for small group discussion to simulate in-person anatomy laboratory experiences. Online anatomical studies had no academic advantage or disadvantage compared to in-person instruction. Additionally, students indicated that the online laboratories were enjoyable and helpful for learning anatomy, rated the guided cadaveric image portion very highly and responded positively to the helpfulness of breakout room sessions in learning anatomy. Based on the results of this study, online delivery of an anatomy laboratory, that was developed to simulate important aspects of in-person learning, can act as a viable alternative-learning platform for anatomical laboratory education.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".