Clues for improvement of research in objective structured clinical examination
Bibliographic record
Abstract
While objective clinical structured examination (OSCE) is a worldwide recognized and effective method to assess clinical skills of undergraduate medical students, the latest Ottawa conference on the assessment of competences raised vigorous debates regarding the future and innovations of OSCE. This study aimed to provide a comprehensive view of the global research activity on OSCE over the past decades and to identify clues for its improvement. We performed a bibliometric and scientometric analysis of OSCE papers published until March 2024. We included a description of the overall scientific productivity, as well as an unsupervised analysis of the main topics and the international scientific collaborations. A total of 3,224 items were identified from the Scopus database. There was a sudden spike in publications, especially related to virtual/remote OSCE, from 2020 to 2024. We identified leading journals and countries in terms of number of publications and citations. A co-occurrence term network identified three main clusters corresponding to different topics of research in OSCE. Two connected clusters related to OSCE performance and reliability, and a third cluster on student's experience, mental health (anxiety), and perception with few connections to the two previous clusters. Finally, the United States, the United Kingdom, and Canada were identified as leading countries in terms of scientific publications and collaborations in an international scientific network involving other European countries (the Netherlands, Belgium, Italy) as well as Saudi Arabia and Australia, and revealed the lack of important collaboration with Asian countries. Various avenues for improving OSCE research have been identified: i) developing remote OSCE with comparative studies between live and remote OSCE and issuing international recommendations for sharing remote OSCE between universities and countries; ii) fostering international collaborative studies with the support of key collaborating countries; iii) investigating the relationships between student performance and anxiety.
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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 | 0.151 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".