A Qualitative Study to Explore Feasibility of Implementing Seeking Safety and Trauma-Sensitive Yoga
Bibliographic record
Abstract
Background: Seeking Safety (SS) is an evidence-based intervention that benefits patients living with co-occurring PTSD and SUD. Trauma Sensitive Yoga (TSY) has been found to be another beneficial treatment for this patient population. In this study, we examine the feasibility of implementing both SS and TSY within a residential addiction treatment program to support this patient population. Methods: We conducted a qualitative prospective evaluation at the Jubilee Treatment Centre in Timmins Ontario from March 2023 to October 2023. Staff members of this center delivered the SS intervention and the TSY was delivered virtually by a certified yoga teacher to the participants. The data sources included patient data, focus groups, and treatment satisfaction surveys to evaluate the effect of this blended intervention on study participants and program staff. A descriptive statistical analysis as well a thematic analysis was conducted to analyze both quantitative and qualitative data, respectively. Results: -test analysis indicated a significant difference in Trauma Symptoms Checklist 40 scores before and after the intervention. Conclusion: This study provided valuable insights into participants' experiences of participating in this innovative intervention. The finding can benefit future researchers who seek to implement similar programs to support individuals with PTSD and SUD.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".