Supporting the Development of a Strengths-Based Narrative: Applying the Leisure and Well-Being Model in Outpatient Mental Health Services
Bibliographic record
Abstract
Strengths-based practice emphasizes the discovery, development, and expression of a variety of strengths as a significant strategy for well-being (Jones-Smith, 2014). The notion of recovery in mental health services refers to living well with mental illness and is often based in creating a self-narrative that includes mental illness but that is not defined by it, or in other words creating a self-narrative of strengths (Onken, Craig, Ridgway, Ralph, & Cook, 2007). The Leisure and Well-Being Model (LWM) (Carruthers & Hood, 2007; Hood & Carruthers, 2007) provides direction for the development of strengths-based therapeutic recreation (TR) programs and this article will describe the development, implementation, and evaluation plan for a TR program designed to address one of the distal goals of the LWM, “cultivation and expression of one’s full potential including strengths, capacities and assets” (Carruthers & Hood, 2007, p. 280) in outpatient mental health services. The literature on recovery in mental health treatment, strengths-based practice, positive psychology and narrative therapy provided the conceptual framework for the application of the LWM to TR services. The resultant program, entitled Be Your Best Self, is described in some detail and the ongoing plans for development and evaluation research are articulated.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.005 |
| 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".