Emerging from the Pandemic: Q‐Method Analysis of the Return to Normalcy in Undergraduate Anatomy
Bibliographic record
Abstract
Introduction As a result of the Covid‐19 pandemic, anatomy education has had to adopt online modes of delivery. Previous research conducted on student views towards an online undergraduate anatomy and physiology course revealed areas of both strong preference (e.g., asynchronous lectures) and strong dislike (e.g., virtual specimens) within the online format. As restrictions are lessened and students transition back into in‐person learning, there exists the unique opportunity to examine the views of a consistent cohort of students towards virtual and in‐person modes of course delivery. Objective The current study seeks to compare the views of a single cohort of students experiencing both the online and in‐person anatomy and physiology course. To avoid the considerable drawbacks of course evaluations relying on Likert scales and traditional qualitative analysis, Q‐methodology is being used to measure the different perspectives of students between the two teaching modalities. Method Q‐methodology will be used to assess opinions of students enrolled in the same undergraduate introductory anatomy and physiology course . A list of 41 opinion‐based statements regarding anatomy education was compiled. Students will sort the statements in a quasi‐normal grid based on their degree of agreement with the statements. The rankings will then undergo a by‐person factor analysis which categorizes students with shared perceptions into groups allowing educators to better understand and respond to the needs of students. Students will have the opportunity to respond to the survey at two points: at the end of the Fall 2021 semester after a primarily online mode of delivery, and at the end of the Winter 2022 semester following a primarily in person mode of delivery. Results Data collection and analysis are anticipated to be completed in March of 2022. Conclusion We hypothesize that Q‐methodology will enable us to discover student views specifically relevant to their experience in both the online and in‐person format of the course. We predict that this study will help identify areas of strength in both virtual and in‐person learning environments. These findings will be useful in integrating the strengths of both learning environments when developing future course offerings.
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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 | 0.058 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".