Feasibility Study of an Embodied and Embedded Mindfulness- and Compassion-Based Intervention for Non-Suicidal Self-Injury Disorder
Bibliographic record
Abstract
Abstract Background Non-suicidal self-injury (NSSI) is associated with suicide risk, as well as a number of psychological disorders. This, coupled with its high prevalence rate, make it imperative that effective treatments for those who engage in NSSI are investigated and made available to the public. Despite this, few interventions specifically targeting NSSI have been researched. In fact, to date, there are no evidence-based treatments for NSSI. This study aimed to address this gap in the existing research by investigating the effectiveness, feasibility, and acceptability of an embodied and embedded mindfulness and compassion treatment (EEMCT) for individuals who engage in self-injury. Methods Six participants attended eight weekly two-hour group therapy sessions. Outcomes measured included urges to self-injure, as well as mental health symptoms commonly associated with NSSI (i.e., depression, anxiety, difficulty with emotion regulation, perceived stress). Outcomes were measured at pre-intervention, post-intervention, and six months follow-up. Results Anxiety decreased significantly from pre-intervention to post-intervention and from pre-intervention to follow-up. Depressive symptoms also significantly decreased from pre-intervention to follow-up, as did emotion regulation. Perceived stress did not show significant change across time points, nor did urges to self-injure. Participants gave feedback about the intervention in semi-structured interviews. They noted benefits of the intervention (e.g., learning self-kindness and awareness of thoughts and feelings), as well as several ways in which the intervention could be improved (e.g., more take-home practice material, shorter meditations). Conclusions
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".