Evaluating the Use of Mindfulness and Yoga Training on Forensic Inpatients: A Pilot Study
Bibliographic record
Abstract
Forensic inpatients (i. e., individuals found not responsible for a criminal offense on account of mental illness) represent an often marginalized and difficult-to-treat population. This has led to the need for research exploring the effectiveness of novel interventions. A Canadian forensic hospital has developed an 8-weeks mindfulness and yoga training program (MTP). This pilot study examined the potential effects of this program on patients' mindfulness, stress, and use of cognitive and emotion regulation strategies. A sample of 13 forensic inpatients (male = 92%) participating in the MTP program completed self-report measures assessing dispositional mindfulness, perceived stress, and use of cognitive emotion regulation strategies at baseline, post-intervention, and a 3-months follow-up. Repeated measure ANOVAs found a significant increase in the describe facet of mindfulness (p = 0.03) with a large effect size (ηp2 = 0.26) and a significant decrease in stress (p = 0.003) with a large effect size (ηp2 = 0.39). Pairwise comparisons revealed medium to large significant changes between baseline and post-intervention for both the describe facet (p = 0.03, Hedge's g = 0.55) and stress (p = 0.003, Hedge's g = 0.70). However, comparisons were insignificant between baseline and follow-up. No significant main effects were found on the use of cognitive emotion regulation strategies. This pilot study offers preliminary support for the use of the MTP as an adjunctive therapy in forensic inpatient treatment. Further investigation is needed into the long-term impacts of this training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".