Is Digital Video Recorded Simulated Suture Skills Testing Associated With Lower Stress and Anxiety Than Traditional In-Person Assessment for Veterinary Students?
Bibliographic record
Abstract
Anxiety can affect exam performance, so exploring methods to improve mental health and academic performance is relevant. The objectives of this study were to investigate stress among veterinary students undergoing simulated suture skills examinations and determine whether digital video recording can reduce stress compared to in-person examination. Thirty-nine students were prospectively enrolled and randomized to undergo two simulated suture examinations, a session proctored by an in-person examiner or one digitally recorded with no proctor present and then crossed over to the other group. Survey data, modified State-Trait Anxiety Inventory (STAI), salivary cortisol, heart rate (HR) and blood pressures were obtained at baseline, prior to, and post-examinations. STAI scores were significantly higher post- in-person examination compared to before in-person examination ( p = .0014) for first session. Pre-examination STAI scores were significantly higher for in-person examinations compared to recorded examinations ( p = .0312) during the second session. Blood pressure was significantly higher during the first session regardless of examination type ( p = .018) and HR was lower at baseline than pre- and post-examination, regardless of exam type ( p < .0001). Students reported more stress with in-person examinations ( p < .0001) and that if given a choice, they would preferentially opt for recorded examinations ( p < .0001). Twenty-eight of 32 students with prior suture skills examination experience reported that the simulated examination was less stressful. STAI scores and self-reported stress levels were significantly lower following recorded exams. Digital video recording of skills testing can reduce perceived stress in veterinary students compared to traditional in-person skills examination.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".