Can relaxation exercises improve students’ OSCE grades: a prospective study
Bibliographic record
Abstract
Introduction: OSCE (Objective Structured Clinical Examination) are a means of assessing health profession students. However, they are a source of stress or anxiety for students. The aim of our study was to improve medical students’ performance during OSCEs by using human performance optimization techniques (HPOT). Methods: Naïve students for OSCE were divided into blocks of five, randomized to HPOT and control groups. Before starting their OSCE circuit, HPOT blocks underwent a 30-minute preparation session. Anxiety was assessed before and after the OSCE using a Visual Analogic Scale (VAS). Results: We randomized and assigned 206 students to 41 blocks of which 20 were HPOT and 21 were control. Anxiety before the exam was significantly reduced thanks to the HPOT procedure with a median value of six and four on the VAS respectively before and after the relaxation session (p = 0.001). The final exam score was not associated with pre-OSCE anxiety (p = 0.5). The HPOT procedure did not improve the final score (p = 0.4). Interestingly, the final score was inversely correlated with the final median anxiety VAS reading after the exam (p = 0.01): students with the lowest anxiety VAS achieved better scores. Conclusion: Relaxation, conscious breathing, and positive reinforcement methods reduced students’ anxiety prior to their OSCE; however, these techniques did not improve their scores.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".