Going Beyond: An Adventure- and Recreation-Based Group Intervention Promotes Well-Being and Weight Loss in Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To undertake a preliminary study to assess the feasibility of clinical implementation and evaluate the effectiveness of a novel adventure- and recreation-based group intervention in the rehabilitation of individuals with schizophrenia. METHODS: In a 2-year, prospective, case-control study, 23 consecutively referred, clinically stabilized schizophrenia patients received the new intervention over an 8-month period; 31 patients on the wait list, considered the control group, received standard clinical care that included some recreational activities. Symptom severity, self-esteem, self-appraised cognitive abilities, and functioning were documented for both groups with standardized rating scales administered at baseline, on completion of treatment, and at 12 months posttreatment. RESULTS: Treatment adherence was 97%, and there were no dropouts. Patients in the study group showed marginal improvement in perceived cognitive abilities and on domain-specific functioning measures but experienced a significant improvement in their self-esteem and global functioning (P < 0.05), as well as a weight loss of over 12 lb. Improvement was sustained over 1 year with further occupational and social gains. CONCLUSION: In the context of overcoming barriers to providing early intervention for youth and preventing metabolic problems among older adults with schizophrenia, adventure- and recreation-based interventions could play a useful complementary role.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".