Responses to and Reflections on Clinical Skills Teaching and Assessment during COVID-19: A Global Survey
Bibliographic record
Abstract
Clinical skills are traditionally taught face-to-face with a focus on hands-on learning. The COVID-19 pandemic forced institutions to adjust their teaching and assessment. This project investigated how veterinary schools adapted clinical skills teaching and assessment, and identified resulting changes and innovations that will progress clinical skills teaching in the future. An online survey was developed and disseminated using QuestionPro. The survey was written in English, translated into French, Spanish and Chinese to encourage international participation, and was open from December 2021 to May 2022. Data were analyzed descriptively and using thematic analysis. Responses came from 91 institutions from 48 countries. During COVID-19, most institutions (70.3%) used a combination of face-to-face and synchronous online classes. Classes were cancelled at certain times by 50.5% of institutions. Almost all institutions (92.3%) provided additional support, including self-directed online learning (e.g., flipped classroom), packs of equipment for students to use at home, online peer tutoring and 'bootcamp' or catch-up sessions. Three themes were identified for beneficial changes to clinical skills teaching that will be kept: the use of the flipped classroom, students having equipment at home for practice and smaller group sizes where possible. During COVID-19, 86.8% of institutions made changes to clinical skills assessments. The use of videos for assessments was identified as a benefit that some institutions would keep. Significant challenges were experienced by teachers, including a high workload. The pandemic inevitably resulted in changes in clinical skills teaching and assessment, but the experiences gained have potential to result in long-term benefits.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".