Emotion regulation predicts recovery capital beyond mindfulness and demographic variation in Recovery Dharma
Bibliographic record
Abstract
Recovery Dharma is a Buddhist-inspired mutual-aid recovery program for those with substance use disorders and behavioral addictions. The program combines meditation, emotion regulation techniques, literature, and Buddhist practices during meetings to help people achieve emotional balance and improve their well-being. Despite the growing popularity of Recovery Dharma, how the practices in this program predict recovery resources remain largely unknown. We conducted a study investigating whether mindfulness and difficulty regulating emotions can predict individuals’ recovery capital - a construct strongly correlated with positive recovery outcomes. Recovery Dharma members (n = 122; 88% White; 45% women) completed two online surveys six months apart. We conducted hierarchical linear regressions and found that mindfulness predicted unique variability in recovery capital. However, our final model that included difficulty regulating emotions explained a significantly larger portion of variability above and beyond demographic variation and mindfulness. In an exploratory analysis, we found that difficulty regulating emotions predicted recovery capital as a unidimensional construct, not any particular subconstruct. The results suggested that Recovery Dharma members’ emotion regulation skills were the strongest predictor of positive recovery outcomes, surpassing demographic characteristics and mindfulness. As such, the intentional cultivation and improvements in emotion regulation skills inherent in Buddhist practices within the Recovery Dharma framework may indicate positive long-term recovery outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".