Naturalistic Evaluation of an Adjunctive Yoga Program for Women with Substance Use Disorders in Inpatient Treatment: Within-Treatment Effects on Cravings, Self-efficacy, Psychiatric Symptoms, Impulsivity, and Mindfulness
Bibliographic record
Abstract
Addiction continues to be a major public health concern, and rates of relapse following currently-available treatments remain high. There is increasing interest in the adjunctive use of mindfulness-based interventions, such as yoga, to improve treatment outcomes. The current study was a preliminary naturalistic investigation of a novel trauma-informed yoga intervention in an inpatient treatment program for women with substance use disorder (SUD). Changes and differences in somatic symptoms, psychiatric symptoms, and psychological mechanisms were evaluated in women receiving treatment-as-usual (n = 36) and treatment-as-usual plus the yoga intervention (n = 42). For both groups, statistically significant within-subjects changes were present for somatic and psychiatric symptoms, cravings, self-efficacy, and multiple facets of impulsivity and mindfulness. Compared to standard treatment alone, participants in the treatment plus yoga condition significantly improved in range of motion and the Lack of Premeditation facet of impulsivity. Although most domains were not selectively affected, these initial within-treatment findings in this naturalistic evaluation suggest some promise for adjunctive yoga and a need for further evaluation, especially using larger samples and longer term follow-up.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".