Objective structured clinical evaluation of clinical competence: an integrative review
Bibliographic record
Abstract
AIM: This paper presents an integrative literature review conducted to describe the utility of the objective structured clinical evaluation (OSCE) as a strategy of measuring one form of clinical competence in nursing. BACKGROUND: The emergence of the OSCE, one form of evaluation of clinical competence used in medicine, is gaining more scrutiny and consideration in nursing education. DATA SOURCES: The review was conducted through an initial search of computerized databases CINAHL, Cochrane Database of Systematic Reviews, Academic Search Premier and MEDLINE for the period from 1960 to 2008. METHODS: An integrative review was performed and 41 papers met the inclusion criteria. RESULTS: The complexities of evaluating clinical competence can be addressed through use of an OSCE process. Concerns related to the conceptual limitations and the lack of psychometric properties of the tools available for measurement in nursing education have been identified. CONCLUSION: Major gaps exist in the nursing literature regarding the examination of the psychometric properties of the OSCE, the suitability of the design of the OSCE structure and tools for nursing to measure clinical competency, and the associated costs in the application of this evaluative method. Research conducted on the psychometric properties of the OSCE tool used and correlations to other evaluative methods currently used to evaluate nursing clinical competence would inform educational practices.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".