Older Adults’ Use of SOC Strategies for Leisure Participation Following an Acute Health Event: Implications for Recreation Service Delivery
Bibliographic record
Abstract
EXECUTIVE SUMMARY: Participating in leisure or recreation pursuits can be an important part of recovering and maintaining health and well-being following an acute health event (e.g., a fall or stroke). However, there is a need to identify how to best support individuals to be successful in community recreation contexts when they live with ongoing limitations. This paper draws on Selective Optimization with Compensation (SOC) theory to examine the ways rural community dwelling older adults were able to continue valued activities after an acute health event. SOC involves overlapping processes of selection, optimization and compensation. Selection involves prioritizing goals or activities to maintain or improve abilities. Optimization refers to strategies to make the most of a chosen pursuit (e.g., monitoring energy) and compensation involves modifying activities or using adaptive aids to compensate for limitations. Although there has been previous attention to the use of SOC in therapeutic recreation practice, there is a need to consider its role in guiding the work of community recreation practitioners who may interact with older adults living with ongoing health challenges. Eleven adults (four males and seven females, ranging in age from 53 to 89) were recruited from two rehabilitation units located in rural health jurisdictions in eastern Canada after being hospitalized for a variety of reasons (e.g., due to a stroke, fall, hip replacement). Three typologies reflected patterns of adaptation following the transition home. For three of the 11 participants, Life as New Normal referred to re-establishing their lives in ways that enabled them to pursue goals based on activity preferences. For five participants, Making the Best of It reflected a resignation that they were trying to make the best of a less than ideal situation. The remaining participants reflected a pattern of Struggling to Survive , wherein they were unable to effectively use SOC processes to maintain participation in valued activities. What can we learn from these experiences for recreation practice? First, any interaction between recreation staff and participants can be an opportunity to support goal setting, problem solving and taking action. Helping people learn SOC strategies can help those who may lack knowledge, skills or confidence to participate independently. Second, creating opportunities for peer support can enhance confidence and motivation. Finally, there is a need for recreation practitioners to communicate and collaborate with health services providers to provide better support to individuals in the transition home following an acute health event.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".