Auricular stimulation vs. expressive writing for exam anxiety in medical students – A randomized crossover investigation
Bibliographic record
Abstract
OBJECTIVE: Auricular stimulation (AS) is a promising method in the treatment of situational anxiety. Expressive writing (EW) is an established psychological method, which reduces test anxiety and improves exam results. The aim of this crossover trial was to compare AS with EW, and with the no intervention (NI) condition, for treatment of exam anxiety. METHODS: Healthy medical students underwent 3 comparable anatomy exams with an interval of one month, either performing EW, receiving AS or NI prior to the exam; the order of interventions was randomized. AS was applied using indwelling fixed needles bilaterally at the areas innervated mostly by the auricular branch of the vagal nerve on the day before the exam. Anxiety level, measured using State-Trait-Anxiety Inventory (STAI) before and after the interventions and immediately before exam, was the primary outcome. Quality of night sleep, blood pressure, heart rate and activity of salivary alpha-amylase (sAA) were analyzed across 3 conditions. RESULTS: All 37 included participants completed the study. Anxiety level (STAI) decreased immediately after AS in comparison with baseline (P = 0.02) and remained lower in comparison with that after EW and NI (P<0.01) on the day of exam. After EW and NI anxiety increased on the day of exam in comparison with baseline (P<0.01). Quality of sleep improved after AS in comparison with both control conditions (P<0.01). The activity of sAA decreased after EW and after AS (P<0.05) but not after NI condition. CONCLUSION: Auricular stimulation, but not expressive writing, reduced exam anxiety and improved quality of sleep in medical students. These changes might be due to reduced activity of the sympathetic nervous system.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".