Implementation of a Formative Objective Structured Clinical Exam to assess self evaluation in a rural BSN-DNP program
Bibliographic record
Abstract
Background and objective: The role of self-assessment in competency-based education has been controversial. The Objective Structured Clinical Exam (OSCE) has been used to assess competencies across the health professions. However, exploring the role of the OSCE as a method of self-assessment for nursing students has been limited. Objective: Implementation of a low cost pilot OSCE in a rural BSN-DNP program to explore graduate nursing students perceived self-evaluation of competencies to their actual OSCE performance.Methods: Eight students enrolled in a small, rural Bachelor of Science and Nursing to Doctorate of Nursing Practice (BSN-DNP) program in the Family Nurse Practitioner (FNP) specialty track were required to complete an OSCE. Graduate students participating in the OSCE completed a Self-Assessment of Competency questionnaire prior to performing the OSCE and the results were compared to their actual performance on the OSCE. Using available resources, undergraduate students in the BSN program at the institution were utilized as standardized patients.Results: Students perceived self-assessment of competence rated higher than their actual performance in subjective and objective data collection and implementation of a plan. Students’ actual performance was superior to their perceived self-assessment regarding communication with the patient.Conclusions: Without competency-based self-assessments, students can be unaware of their strengths and weaknesses. The OSCE is an instrument that provides faculty and students with objective measures of self-evaluation and should be considered as a component of competency-based education in rural nursing institutions.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".