Mindfulness Practice Reduces Hair Cortisol, Anxiety and Perceived Stress in University Workers: Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Anxiety and stress are common mental health conditions reported by university workers. Practices of mindfulness represent one promising approach as an effective and feasible means to reduce stress, improve mental health and promote well-being; however, there are no clinical trials that have combined long-term stress biomarkers (hair cortisol) and psychometric assessments in a sample of university workers. OBJECTIVE: This study investigated the effectiveness of a mindfulness-based program on long-term stress, by measuring hair cortisol concentration and perceived stress and anxiety among workers who were undergoing high levels of stress. METHOD: We conducted a randomized clinical trial at work among the employees of a public university. We compared a group that received the eight-week mindfulness intervention with the wait list group who received no intervention. RESULTS: A total of 30 participants were included in the study, with n = 15 subjects in the intervention group and n = 15 in the control group. Hair cortisol, perceived stress and anxiety significantly reduced after the intervention compared to the control group, which had no appreciable decline in the measured variables. CONCLUSION: This clinical trial showed the effectiveness of a mindfulness program on mental health psychometric measures (perceived stress and anxiety) and on a long-term stress biomarker (hair cortisol). It can be concluded that an eight-week mindfulness program could be implemented as an effective strategy to reduce stress biomarkers (hair cortisol) as well as perceived stress and anxiety, improving the mental health of university workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".