Leisure-Generated Meanings and Active Living for Persons With Mental Illness
Bibliographic record
Abstract
Leisure may potentially play a key role in rehabilitation counseling, including psychiatric rehabilitation. Based on recovery and positive psychology frameworks in which meaning-making is a central concept, this study examined the role of leisure-generated meanings (LGMs) experienced by culturally diverse individuals with mental illness in potentially helping them better cope with stress, adjust to and recover from mental illness, as well as feel more actively engaged in life. One-on-one survey interviews were conducted with African ( n = 35), Hispanic/Latino ( n = 28), Caucasian ( n = 28), and Asian ( n = 8) American adults (aged between 23 and 78) (total n = 101) with mental illness (e.g., bipolar disorder, n = 32; major depression, n = 23; schizophrenia, n = 22) in Philadelphia, Pennsylvania. Using general linear modeling, we found that LGMs significantly predicted the adjustment to and recovery from mental illness, leisure stress-coping, leisure satisfaction, and perceived active living positively, and lower leisure boredom. The findings have implications for psychiatric rehabilitation to better support persons with mental illness from a strengths-based, meaning-centered, and active-living promotion perspective in which leisure seems to play an important 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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".