Remote Assessment of Veterinary Clinical Skills Courses During the COVID-19 Pandemic
Bibliographic record
Abstract
In spring 2020, the COVID-19 pandemic forced educators to adjust the delivery and assessment of curriculum. While didactic courses moved online, laboratory courses were not amenable to this shift. In particular, assessment of clinical skills courses through common methods including objective structured clinical examinations (OSCEs) became inadvisable. This article describes decisions made for first-, second-, and third-year veterinary students ( n = 368) with respect to clinical skills at one US college. This includes the remote completion of a surgical skills curriculum using instructional videos and models and the delaying of laboratory sessions deemed impossible to deliver remotely. First- and third-year students were subsequently assessed using modified remote OSCEs. Second-year students were assessed using the standard surgical skills examination, video-recorded. All first- and third-year students successfully passed their OSCE upon either first attempt or remediation. Two second-year students failed their remediation examination and were offered additional faculty tutoring and another remediation attempt at the start of the fall semester. The remediation rate on the surgical skills examination was not different from that of previous years. One incident of suspected academic dishonesty occurred in the first-year OSCE. Students learned surgical skills successfully at home by practicing on models and receiving feedback of their skills on video recordings. While disappointing, one case of academic dishonesty among the 368 total students tested was not surprising. Remote assessment using modified OSCEs and surgical skills exams appears feasible and fair when in-person testing is not possible.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".