Nursing students perspectives and academic performance in anatomy and physiology before, during and after a stability period of COVID-19
Bibliographic record
Abstract
The Coronavirus disease 2019 (COVID-19) has dramatically disrupted medical, allied health, and nursing education worldwide. It has created challenges for students and educators by requiring a sudden shift to online teaching and learning activities from didactic, passive teaching and learning. The objective of the present study was to evaluate the impact of these modes of teaching and learning before, during, and after the transition through COVID-19 on the class average and Grade Point Average (GPA) of nursing students taking anatomy and physiology in the first year of nursing. Using the virtual teaching and learning modality, the present study demonstrated that the mean class average of anatomy and physiology midterms and final examinations during COVID-19 (synchronous online teaching) was significantly higher (P<0.001) compared with the pre-COVID-19 (face-to-face (F2F) teaching) class average. However, the class average and GPA were not different between pre-COVID-19 (F2F teaching) and post-COVID-19 (hybrid/flex teaching). Virtual teaching of these subjects also significantly (P<0.001) increased the students’ GPA in anatomy and physiology during COVID-19 compared to before and after the stability of COVID-19. Students’ perspectives on teaching and learning these courses using these teaching modalities indicated that nursing students prefer a synchronous, hybrid mode of learning in anatomy and physiology. The present study demonstrates nursing students’ preference for a synchronous, online and hybrid mode of teaching and learning anatomy and physiology in case of the re-emergence of a new strain of coronavirus after Omicron variant in future lockdown due to 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".