Virtual delivery: a panacea for the financial and ethical challenges associated with physiology laboratory classes?
Bibliographic record
Abstract
There has been a gradual shift in the delivery of physiology laboratory classes over the last 30 years. For many, wet-lab demonstrations using animal tissues have been reduced or replaced with student-led investigations where students are both subjects and researchers. Despite these changes, expectations remain that physiology courses should include a practical component to encourage deeper and higher-order learning. Wet-lab tissue experiments and student-based group research formats can be expensive to run, associated with various ethical constraints, and, as discovered in these times of COVID-19, difficult to operate while adhering to physical distancing. We address the proposition that online and/or remote delivery of laboratory classes using digital technologies may provide a solution to both financial and ethical constraints of on-campus laboratory classes. Our discussions, as an international group of 10 physiologists from the United States, the United Kingdom, Canada, and Australia, revealed that although some of the financial and ethical constraints of using animal tissues and student-led investigations were addressed by the introduction of online alternatives, the construction and maintenance of online delivery modes could also be expensive and ethical issues, not previously considered, included digital equity and student data security. There was also a collective perception that if face-to-face laboratory classes were changed to an entirely virtual mode there was a risk that some intended learning outcomes would not be met. It was concluded that the "ideal" approach is likely a hybrid model whereby student attendance in face-to-face, on-campus classes is supported with interactive digital content either developed in house or obtained through third-party providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".