International educators’ attitudes, experiences, and recommendations after an abrupt transition to remote physiology laboratories
Bibliographic record
Abstract
The COVID-19 pandemic triggered university lockdowns, forcing physiology educators to rapidly pivot laboratories into a remote delivery format. This study documents the experiences of an international group of 10 physiology educators surrounding this transition. They wrote reflective narratives, framed by guiding questions, to answer the research question: "What were the changes to physiology laboratories in response to the COVID-19 pandemic?" These narratives probed educators' attitudes toward virtual laboratories before, during, and after the transition to remote delivery. Thematic analysis of the reflections found that before COVID-19 only a few respondents had utilized virtual laboratories and most felt that virtual laboratories could not replace the in-person laboratory experience. In response to university lockdowns, most respondents transitioned from traditional labs to remote formats within a week or less. The most common remote delivery formats were commercially available online physiology laboratories, homemade videos, and sample experimental data. The main challenges associated with the rapid remote transition included workload and expertise constraints, disparities in online access and workspaces, issues with academic integrity, educator and student stress, changes in learning outcomes, and reduced engagement. However, the experience generated opportunities including exploration of unfamiliar technologies, new collaborations, and revisiting the physiology laboratory curriculum and structure. Most of the respondents reported planning on retaining some aspects of the remote laboratories postpandemic, particularly with a blended model of remote and on-campus laboratories. This study concludes with recommendations for physiology educators as to how they can successfully develop and deliver remote laboratories.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".