Objective Structured Clinical Examination (OSCE) als kompetenzorientiertes Prüfungsinstrument in der pflegerischen Erstausbildung
Bibliographic record
Abstract
Background: In response to demographic trends in Germany nursing competencies are currently reevaluated. Since these have to be taught and trained in nursing education programs, efficient verification of the success is necessary. OSCEs are internationally well-recognized as a comprehensive tool for that. Aim: In this analysis we identified competencies worldwide, which are tested by OSCEs in undergraduate nursing education programs. Method: An international literature research was conducted. The selection criterion for an article was the specification of at least one verifiable competency. Afterwards the competencies were categorized into knowledge, skills and attitudes according to the German “Fachqualifikationsrahmen Pflege für die hochschulische Bildung”. Results: A total of 36 publications fulfilled all inclusion criteria. Relevant studies were predominantly initiated in the UK, Canada and Australia. Within all categories a total of n = 166 different competencies are mentioned. OSCEs are developed and performed in a broad range of methods. Most frequently skills were verified. The most common topic was sure handling of medication. Other important themes were communicative competencies in relation to patients and the ability of self-evaluation. Discussion/Conclusions: A variation in examination methods is appropriate as different competencies are acquired in preparation of the test. Evaluation took place on an individual or institutional level. Further research is needed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 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".