Why So Stressed? A Descriptive Thematic Analysis of Physical Therapy Students' Descriptions of Causes of Anxiety during Objective Structured Clinical Exams
Bibliographic record
Abstract
Purpose: The purpose of this study was to collect and conduct a descriptive content analysis of the primary triggers of practical exam anxiety in Master of Physical Therapy (MPT) students in a Canadian university programme. Method: First and second-year MPT students were invited to reflect upon their top 5 sources or triggers of OSCE exam anxiety, collected in written format during a low-stress, low-examination period of their programme. All participants had participated in at least 3 OSCEs before providing data. The emergent themes were member-checked with 10 of the original participants to improve trustworthiness of the results. Results: 56 of a possible 105 students provided 224 triggers of OSCE anxiety. Thematic content analysis revealed 6 emergent meta-themes that adequately captured all triggers. They were: social performance anxiety, fear of lacking competence, overvaluing the outcome, fear of the unknown, impaired personal health/coping resources, and operational/procedural influences. These meta-themes were endorsed by the participant sub-group. Conclusions: OSCEs are common forms of evaluation in MPT training programmes, but are also highly anxiogenic. The first step toward mitigating exam anxiety, thereby ensuring exam performance is less confounded by anxiety, is to identify the common triggers. Confidence in results will be strengthened by replication in other programmes.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| 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".