Leisure and Leisure Education as Resources for Rehabilitation Supports for Chronic Condition Self-Management in Rural and Remote Communities
Bibliographic record
Abstract
The potential of leisure (enjoyable free time pursuits) to be a resource for chronic condition self-management (CCSM) is well-established. Because leisure pursuits are often self-determined, they have the potential to allow people to not only address self-management goals (e.g., managing symptoms through movements or stress-reducing activities) but meet important psychosocial needs (e.g., affiliation, sense of mastery) as well as support participation in a range of meaningful life situations. In this "Perspective" piece, we advocate for the ways leisure and leisure education can be resources for rehabilitation professionals to support CCSM, especially in rural and remote communities. In particular, we focus on aspects of the Taxonomy of Everyday Self-Management Strategies [TEDSS (1)] to highlight ways that embedding leisure and leisure education into supports for CCSM can strengthen rehabilitation services offered to rural and remote dwelling adults living with chronic conditions. Recognizing the breadth of leisure-related resources available in rural and remote communities, we recommend the following strategies to incorporate a focus on leisure-based self-management within rehabilitation services: (a) enhance the knowledge and capacity of rehabilitation practitioners to support leisure-based CCSM; (b) focus on coordinated leadership, patient navigation, and building multi-sectoral partnerships to better link individuals living with chronic conditions to community services and supports; and (c) educate individuals with chronic conditions and family/carers to develop knowledge, skills, awareness and confidence to use leisure as a self-management resource.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".