Yoga for Healing: An Analysis of the Effects of Yoga Practices On Military Victims of Post-Traumatic Stress Disorder
Bibliographic record
Abstract
The practice of yoga has been associated with healing and well-being for centuries. With the recent surge in its popularity, research to yoga’s healing properties have found many ways in which it can be incorporated into a wide variety of treatment programs. One area of interest is the treatment of military combat veterans with post-traumatic stress disorder (PTSD). Research into the use of yoga as supplemental treatment through its interdisciplinary psychophysical effects and mindfulness training have found it to be extremely beneficial. In this article, the psychophysical aspects of yoga and how they interact the physical and psychological symptoms of PTSD are discussed and linked with the treatment plans used for military veterans. It also addresses the issues of the specific military culture and the pressures associated with fulfilling the valorous role of the perfect solider. Physical effects of yoga help patients with alleviating allosteric load to increase the chances of a healthy recovery and maintaining homeostasis, while the psychological effects include increased levels of mindfulness that help veterans complete trauma-focused cognitive behavioural therapy (TFCBT), as well as by regaining their sense of self-control and mastery of psychological processes. With the prevalence of this disorder in the military population along with the comorbidity of suicide, new alternative treatments should be considered to decrease the number of deaths from this devastating disorder. Thus, the psychophysical effect of yoga’s mind-body connection incorporated into military culture can be beneficial to those who have done the ultimate sacrifice for the safety of our country.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".