The Felt Sense of Interconnectedness: A qualitative analysis of perceptions on finding resilience in the aftermath of trauma using the mind-body connections of Yoga
Bibliographic record
Abstract
Members and veterans of the military are at an increased risk of exposure to traumatic experiences due to the very nature of their occupation. The most recent statistics on Canada’s deployment to Afghanistan show that 13.2% of the (Canadian Armed Forces (CAF) members deployed have been diagnosed with a mental injury within a five year follow up period of redeployment from the theatre of operations. The present preliminary study was designed to examine Yoga as a therapeutic intervention for trauma in a population of CAF members and veterans. The author interviewed 4 service providers and 2 service users of Yoga-based therapeutic interventions specifically designed for members/veterans with a diagnosis of Posttraumatic Stress Disorder (PTSD). The participants discussed the importance of connection to something greater than the Self as an absolute in building posttraumatic resilience, and both groups offered the mind-body connection as paramount in healing trauma and as the vehicle for the individual practitioner to come to know the felt sense of spirit and connection. These findings have implications for possible therapeutic interventions for CAF members, as well as for future research possibilities in the field of posttraumatic resilience and growth.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".