Creating and implementing objective structured clinical exams for use in pediatric nursing education in Ghana: Reflections and lessons learned
Bibliographic record
Abstract
The use of the Objective Structured Clinical Exam (OSCE) to measure the clinical competency of health care professionals is well established in many training institutions globally because of their high validity and reliability. In the context of a pediatric nursing partnership program in Ghana (the SickKids-Ghana Pediatric Nursing Education Partnership [PNEP]), we aimed to develop a national, accredited, competency-based curriculum. The integration of the OSCE into the curriculum was novel in the Ghanaian setting. It served as a standardized method for student assessment of higher clinical competencies, including both skills and attitudes, compared to conventional methods. Using mixed-methods, we previously found that the PNEP curriculum effectively increased graduates’ knowledge, confidence, and clinical skill in pediatric nursing. This manuscript aims to reflect on the development of the OSCE stations, case scenarios, and rating materials, with particular reference to the methodological approach and lessons learned. We further reflect on the feedback from faculty and students pertaining to the usefulness of the OSCE as an assessment tool and the inclusion of standardized patients to assess communication skills. Adaptations required to safely conduct the OSCEs in the context of the COVID-19 pandemic have also been highlighted. Our findings throughout the OSCE development, implementation, and testing processes in Ghana could aid and inform similar tool development for nursing education institutions in the West African region and other resource-limited settings.
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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 | 0.065 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".