A systematic review of yoga interventions for helping health professionals and students
Bibliographic record
Abstract
Helping Health Professionals (HHP) and HHP students are among the highest risk occupational groups for compromised mental and physical health. There is a paucity of information regarding preventive interventions for mental and physical health in this group of healthcare providers. OBJECTIVE: The objective of this review was to examine the effectiveness of yoga interventions for the prevention and reduction of mental and physical disorders among HHPs and HHP students. DESIGN: An exhaustive systematic search was conducted in May 2020. Databases searched in the OVID interface included: MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Embase, and PsycINFO. EbscoHost databases searched included: CINAHL Plus with Full Text, SPORTDiscus with Full Text, Alt HealthWatch, Education Research Complete, SocINDEX with Full Text, ERIC, and Academic Search Complete. Scopus was also searched. RESULTS: The search yielded 4,973 records, and after removal of duplicates 3197 records remained. Using inclusion and exclusion criteria, titles and abstracts were screened and full text articles (n = 82) were retrieved and screened. Twenty-five studies were identified for inclusion in this review. Most frequently reported findings of yoga interventions in this population included a reduction in stress, anxiety, depression, and musculoskeletal pain. CONCLUSION: It is our conclusion that mental and physical benefits can be obtained through implementation of yoga interventions for HHPs and HHP students across a variety of settings and backgrounds. However, researchers would benefit from following recommended guidelines for the design and reporting of yoga interventions to improve study quality and rigour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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 teacher head, 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".