Student and instructor perspectives following a virtual objective structured clinical examination.
Bibliographic record
Abstract
Background: In March 2020, COVID-19 public health restrictions impeded in-person clinical assessment. In response, a dental hygiene program administered a virtual objective structured clinical exam (vOSCE) using Zoom to assess student competency in performing a health history. This study aimed to explore the vOSCE experience from both student and clinical instructor perspectives. Methods: This 2-part cross-sectional study gathered student and clinical instructor perceptions of the vOSCE. Forty-two students were invited to complete an online questionnaire. Basic descriptive statistics reporting percentages were tabulated. Twelve clinical instructors were invited to participate in focus groups, which were audiorecorded and transcribed verbatim. Qualitative data were analysed using inductive thematic analysis. Results: Questionnaires were received from 23 (55%) students. Students supported (91%) the vOSCE experience and believed it assessed their knowledge (87%), their ability to complete a health history (91%), and ability to communicate effectively (87%). Students reported high agreement (87%) with how the Zoom platform facilitated the examination. Some students (35%) felt the vOSCE was more stressful than an in-person OSCE. However, 43% indicated it wasn't more stressful. Focus groups with clinical instructors revealed perspectives on using vOSCEs, which were captured under 4 themes: preparation, assessment suitability, authenticity, and future considerations. Conclusion: Based on student and instructor perspectives, vOSCEs could be a viable alternative to in-person OSCEs for health history evaluations. As technology applications continue to emerge for conducting virtual examinations, there may be increased use of and ease of use with a virtual platform to conduct other types of clinical evaluations.
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 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".