Evaluation of a low cost OSCE in family nurse practitioner students: An emphasis on self-assessment in competency-based education
Bibliographic record
Abstract
Objective: The American Association of Colleges of Nursing has identified competency-based education (CBE) as a priority in nursing education. The Objective Structured Clinical Examination (OSCE) has been used across health professions as a tool to incorporate competency-based education. However, the OSCE has been correlated with intensive faculty resources and high costs. The objective of this study was to discuss the evaluation of a low cost OSCE and its ability to incorporate the role of self-assessment in competency-based education within a nurse practitioner program.Methods: Faculty at a public university developed and evaluated an OSCE, exploring its implementation as a component of CBE while minimizing costs using a quasi-experimental design. Nine nurse practitioner students in their third year of a BSN-DNP program completed a pre- and post- assessment of their perceived ability in three OSCEs. Undergraduate nursing students were recruited for the standardized patient role. The OSCEs were recorded for evaluation by faculty and for self-evaluation by the students.Results: There was no significant difference noted in student self-evaluations pre- and post-assessment. There was a statistical difference in the faculty ratings of the student in the otitis media OSCE, with the student rating their performance higher than faculty. There was no statistical difference noted in either the women’s health or hypertension assessments.Conclusions: Recordings of the OSCEs allowed students to identify strengths and weaknesses, cultivating the practice of self-assessment. Integration of minimal cost OSCEs provides opportunities for programs with varying budgets to incorporate it as a component of CBE.
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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 | 0.017 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".