Psychiatric Leisure Rehabilitation: Conceptualization and Illustration.
Bibliographic record
Abstract
Leisure or recreational rehabilitation is not much developed in psychiatric rehabilitation. I present some definitions and a (very) brief history of the notion of leisure, as well as the role of leisure in health. I then describe a novel classification of leisure activities relevant to people with psychiatric disabilities. Following that, I conceptualize the process of psychiatric leisure rehabilitation, illustrated by a case study. I conclude with the benefits of psychiatric leisure rehabilitation and with suggestions for further study and development in this field. Leisure or recreational activities are central in modern life. Not so for persons who have serious and disabling mental disorders. This is manifest even in psychiatric rehabilitation, which mostly addresses vocational and residential--rather than leisure--environments (Anthony, Cohen, Farkas & Gagne, 2002; Corrigan, 2003). Yet leisure is important for persons with psychiatric disabilities, perhaps especially so for those who are not successful or satisfied in vocational environments. This paper outlines a conceptual framework for psychiatric leisure rehabilitation, based on a novel classification of leisure activities, and illustrates this with a case vignette. But first, some definitions and a (very) brief history of the notion of leisure is in order, as well as a presentation of facts about the role of leisure in health (physical and mental).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".