THE PRELIMINARY ANALYSIS OF YOGA THERAPY ON BURNOUT AMONG MEDICAL STAFF: A SINGLE-ARM PRE-POST INTERVENTION
Bibliographic record
Abstract
Abstract Background Burnout, encompassing emotional exhaustion, disrupts the balance between the mind and body, emphasizing the need for concise and practical interventions to mitigate risk in medical settings. Yoga therapy has been shown to alleviate anxiety and depression in both healthy adults and patients with severe mental illnesses. However, there is a paucity of research investigating the effects of short- term yoga therapy on burnout among medical staff. Aims & Objectives This study aims to evaluate the effect of a two-week yoga therapy on burnout among medical staff. Methods We conducted a two-week single-arm pre-post intervention study involving medical and co- medical staff working at the general hospital of Tokyo Dental College, Chiba, Japan. This study protocol received approval from the Institutional Review Board (IRB) of the general hospital of Tokyo Dental College (UMIN:000044465). The intervention comprised in-person and online yoga sessions held twice weekly for four sessions, each lasting 60 minutes. The primary outcome measures included the number of participants, completion rate, and the Client Satisfaction Questionnaire 8 (CSQ-8). The secondary outcomes encompassed the assessment of burnout levels using the Maslach Burnout Inventory General Survey (MBI-GS), blood pressure, heart rate variability, alpha-amylase activities, the Patient Health Questionnaire-9 (PHQ-9), the General Anxiety Disorders-7 (GAD-7), the Pittsburgh Sleep Quality Index (PSQI), the EuroQol-5 dimensions classification system (EQ-5D), the Sheehan Disability Scale (SDS), and the Resilience Scale (RS). These assessments were conducted at registration, the end of the first session, the end of the fourth session, and one-week and four-week follow-up. This study is currently ongoing and aims to recruit fifty participants. A preliminary statistical analysis was performed using R, employing analysis of covariance and repeated-measures analysis of variance. Results Preliminary analysis results were reported herein. Between July 2021 and the present, 21 participants (mean age: 42.2±9.5 years old, 9.5% male) were enrolled in this intervention, with 19 participants (90.4%) completing all sessions. The mean CSQ-8 was 27.7±3.7. Although the total score of MBI-GS did not show significant improvement, the score of GAD-7 decreased from 5.2±3.2 to 2.8±2.8 (p<0.05), the score of PHQ-9 decreased from 6.7±4.0 to 3.2±4.0 (p<0.05), and EQ-5D increased from 67.1±15.5 to 75.2±16.4 (p<0.05). These improvements were sustained at the 4-week follow-up assessments. However, other measurements did not show significant improvement during the short-term period. Discussion & Conclusion This pre-post study, based on a preliminary analysis of 21 participants, highlights the positive effects of a 2-week Hatha yoga intervention on anxiety, depression, and quality of life (QoL) among medical staff in a general hospital. Our findings demonstrate the potential clinical utility of hybrid yoga sessions in mitigating the risk of burnout among medical staff. However, the transient nature of the observed clinical gains underscores the need for further investigations into potential strategies for enhancing these acute effects. This pre-post study has limitations, including a small sample size (n=21) and a shorter session module. Future investigations exploring the optimal intervention are necessary to achieve sustained therapeutic effects of hybrid yoga intervention on burnout.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".