Assessment practices and types of knowledge in two clinical examination formats in nursing education
Bibliographic record
Abstract
Background and objective: The assessment of nursing students' clinical competencies is a global concern, as different exam formats emphasize different types of knowledge and skills. There is a lack of research that uncovers the linkage between clinical exam formats, assessment practices and types of knowledge tested. This study investigates how two different formats of clinical exams—one based on written assignments (control exam), and one conducted in real patient situations (intervention exam)—influence educators’ assessment practices and the types of knowledge they enhance or limit respectively.Methods: The study applied a comparative, ethnographic design, incorporating participant observations, focus group interviews with educators, and grade analysis of 104 nursing students. The analytical framework was informed by Institutional Ethnography (IE) and Donald Schön’s concepts of reflection in practice.Results: The control exam is predictable and controlled facilitating assessment of theoretical knowledge and reflection-on-reflection-in-action but is detached from real-life patient interactions. In contrast, the intervention exam is unpredictable and complex emphasizing assessment of knowing-in-action and reflection-in-action but poses challenges in assessing theoretical reasoning and reflection-on-reflection-in-action. Despite these differences, no significant variation was found in students’ final grades between the two formats.Conclusions: The findings highlight the impact of exam formats on assessment practices and suggest that nursing education should incorporate diverse assessment methods to balance theoretical rigor with clinical competence.
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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 | 0.023 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".