The mySELF group: Recreation- and art-based group therapy as adjunct treatment for posttraumatic stress disorder
Bibliographic record
Abstract
An emerging body of literature demonstrates the benefits of recreation and creative arts therapies as adjunct treatment for service members and Veterans diagnosed with posttraumatic stress disorder (PTSD) and other mental health conditions. The my Social life, Expression, Leisure, and Food (mySELF) group was created as an adjunct therapy option for clients receiving treatment at an operational stress injury clinic for military and Royal Canadian Mounted Police with service-related mental health conditions. This 12-week group was run by a multidisciplinary team that included psychology, nursing, therapeutic recreation, Veteran arts, nutrition services, and music therapy. Facilitators provided a variety of recreation and leisure opportunities, such as woodworking, Tai Chi, and guitar. To date, 41 clients have completed the mySELF program, and 36 provided pre- and post-questionnaire data. Most had a diagnosis of PTSD and had been in treatment for an average of 3.0 (SD = 2.8 y). Pre-, post-, and follow-up (four months post-group) testing revealed significant improvements in leisure attitudes, environmental quality of life, depression, anxiety, stress, and PTSD symptoms. These promising preliminary results suggest that recreation and art can enhance mental health treatment outcomes for military members and police.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".