Yoga and Canadian Armed Forces members' well-being: an analysis based on select physiological and psychological measures
Bibliographic record
Abstract
Introduction: Post-traumatic stress disorder (PTSD) is a psychiatric illness that may develop following a traumatic event or a situation involving the threat of death or serious injury to oneself or others. PTSD is often comorbid with other mental and physical health conditions, such as depression, anxiety, and chronic pain. Several therapeutic, pharmaceutical, and non-traditional interventions are being investigated to eliminate or reduce the severity of these comorbidities in those who suffer from PTSD. The current study investigated the effect of yoga on individuals who did, or did not, screen positively for PTSD on their self-reported symptoms of depression, anxiety, and anger. We also examined perceived physical pain, sleep disturbances, and mental and physical health–related quality of life. Methods: Participants ( n=45) were active or retired members of the Canadian Armed Forces; there were 35 males and 10 females, who self-identified as having experienced at least one traumatic operational event. Participants were screened for PTSD and completed a series of questionnaires before and after 12 weekly yoga sessions. Results: There were statistically significant improvements in levels of anger, anxiety, and pain and in quality of sleep in post-yoga scores compared to baseline. Individuals who met the PTSD screening criteria showed significantly greater improvement than those who did not. Discussion: Although future research is needed, this study supports previous findings that weekly yoga sessions may contribute significantly to reducing the severity of some physical and psychological conditions. Our study also shows that yoga may be particularly effective in individuals with PTSD.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".