Virtual objective structured clinical examination for family nurse practitioner students using a Zoom platform in the time of COVID-19
Bibliographic record
Abstract
Objective: In response to school shut downs amid the COVID-19 pandemic, nurse educators from the University of Southern California implemented a virtual objective structured clinical examination (OSCE) using standardized patients (SPs) to assess family nurse practitioner (FNP) students’ clinical and communication skills as an alternative to the traditional in-person OSCE format. The intent of this paper is to share the nurse educators’ experiences with the transitional process and students’ feedback about their virtual OSCE experiences.Methods: Students (N = 36) enrolled in a childbearing/childrearing clinical course participated in the virtual OSCE using Zoom. The experience included briefing and debriefing sessions. Students were evaluated for their communication and clinical decision making skills based on their assessment of two adolescent patients: one acute with behavioral problems presenting for a checkup and one with headache.Results: All students who participated in the virtual OSCE experience demonstrated appropriate clinical and communication skills. Students perceived the virtual OSCE as a realistic model for telehealth but missed social interaction with faculty and peers and found their inability to conduct physical exam maneuvers challenging. The majority (79.3%) preferred interacting with patients face-to-face.Conclusions: Virtual OSCEs used as low-stakes formative assessments provide FNP students with effective and valuable learning experiences. Transitioning from in-person to virtual OSCEs using Zoom is feasible but requires extensive collaboration between nursing educators and those with access to simulation facilities, such as faculty from schools of medicine. Findings from this experience will serve as a guide for deliberate process improvements for future iterations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".