Leisure as a Coping Resource for Parent Caregivers of Children Living with Autism
Bibliographic record
Abstract
This qualitative study explored the lived experiences of life challenges and leisure as a stress-coping resources among parent caregiver of children living with autism. These parent caregiver experiences were obtained through in-depth interviews of four mothers of children living with autism. Data were then qualitatively analyzed to ascertain meaningful themes. The results of data analysis demonstrated that parent caregivers face a number of barriers related to their leisure participation in four areas: (a) caregiving responsibilities and demands, (b) COVID-19 related barriers (c) time-related barrier and (d) interpersonal barrier. Although the participants of this study identified several barriers as parent caregivers, the findings show that they were able to negotiate some of these barriers to participate in their leisure experiences that enhanced their stress-coping efforts. The findings also revealed that leisure was used as a stress-coping resource in four ways: (1) rejuvenation through leisure, (2) mood enhancement through leisure, (3) distance from stressors through leisure, and (4) social experiences through leisure. This study discussed the importance of recreation therapists advocating for leisure education and casual forms of leisure among parent caregivers for effective coping with caregiver-related stress. This study provided practical implications for recreation therapists and other health care professionals in a related field to better understand the unique needs of this population and encourage leisure participation as a resource of stress-coping.
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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 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".