Give me a break! Addressing observed structured clinical exam anxiety
Bibliographic record
Abstract
WHAT WAS THE EDUCATIONAL CHALLENGE?: Medical students experience high rates of anxiety; frequent examinations are one contributing source. Students may perceive the observed structured clinical examinations (OSCEs) as particularly stressful. Strategies to reduce anxiety during OSCEs have not been described. WHAT WAS THE SOLUTION?: We sought to implement and evaluate a simple, in-the-moment intervention aimed at reducing students' OSCE-related anxiety by making stress-reducing activities available during break stations during a summative pediatric OSCE. HOW WAS THE SOLUTION IMPLEMENTED?: Three break stations were included in an end-of-rotation, summative OSCE. Students were block-randomized to either control group with standard break stations, or intervention group with stress-reducing activities available in the break room. All participants completed the State-Trait Anxiety Inventory (STAI) before and after the OSCE, and a short questionnaire after OSCE completion. WHAT LESSONS WERE LEARNED THAT ARE RELEVANT TO A WIDER GLOBAL AUDIENCE?: Third-year medical students have high levels of stress before and after OSCEs. More than half of students in the intervention group felt their anxiety improved with activities. While the inclusion of stress-reducing activities in break stations did not impact exam performance, some students subjectively felt their performance improved. If OSCE break stations are logistically required, they can be employed to allow students to briefly relax during a high-stress exam without negatively impacting performance. WHAT ARE THE NEXT STEPS?: Next steps include exploration of opportunities for integration of stress-reducing activities during OSCEs with other learner groups, and identification of other stress-inducing aspects of medical training to provide similar opportunities.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".