Mindfulness and Metta-based Trauma Therapy (MMTT): Initial Development and Proof-of-Concept of an Internet Resource
Bibliographic record
Abstract
Trauma and stressor-related disorders, including post-traumatic stress disorder (PTSD) and related comorbid disorders such as anxiety, depression, and dissociative disorders, are difficult to treat. Mindfulness-based clinical interventions have proven efficacy for mental health treatment in face-to-face individual and group modalities, although the feasibility and efficacy of delivering these interventions via the internet has not been evaluated. The present research developed mindfulness and metta-based trauma therapy (MMTT) as an internet resource to support the practice of mindfulness and metta (lovingkindness) meditations for self-regulation and healing from trauma and stressor-related disorders. In the present “proof-of-concept” study, research participants ( n = 177) recruited online practiced mindfulness and metta meditations and related therapeutic exercises available via the website and rated their perceived credibility as interventions for improving self-regulation and well-being and reducing PTSD symptoms, anxiety, depressive, and dissociative experiences, as well as their experienced ease, helpfulness, and informational value. Results suggest that, independent of level of self-reported current and past psychiatric history and PTSD symptoms, participants considered the MMTT website as a credible and helpful therapeutic intervention for improving self-regulation and well-being and reducing PTSD, anxiety, depression, and dissociation. Overall, participants considered guided and non-guided meditation practices more helpful than a journaling exercise, and participants with increased PTSD symptoms preferred metta (lovingkindness) meditations less than other participants. We conclude that MMTT should be piloted in clinical trials as an adjunctive intervention to evidence-based treatments for persons with mood, anxiety, and trauma and stressor-related disorders, as well as more generally as an online resource to support self-regulation and well-being practices.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".