Evaluation of a Mindfulness-Based Stress Reduction (MBSR) Program for Caregivers of Children with Chronic Conditions
Bibliographic record
Abstract
OBJECTIVES: Given the demands of caring for chronically ill children, it is not surprising that caregivers often experience high levels of chronic stress. A Mindfulness-Based Stress Reduction (MBSR) program may offer relief to these caregivers by providing tools for self-care and heath promotion that otherwise may be lacking. METHODS: MBSR classes were offered without restriction to parents of children attending various clinics at a large urban children's medical centre. Caregivers completed the Profile of Mood States (POMS) and Symptoms of Stress Inventory (SOSI) both before and after program participation. RESULTS: Forty-four caregivers participated in one of seven group MBSR sessions that were offered between August 2001 and February 2004. Most were mothers of children with special needs and various chronic conditions, who had been diagnosed an average of 7 years previous. Prior to the intervention, caregivers reported very high levels of stress and mood disturbance. These decreased substantially over the 8-week program, with an overall reduction in stress symptoms of 32% (p < .001), and in total mood disturbance of 56% (p < .001). CONCLUSIONS: This brief MBSR program for caregivers of chronically ill children was successful in significantly decreasing substantial stress symptoms and mood disturbance. Further studies would benefit from using more rigorous methodology and applying the program to other groups of chronically stressed caregivers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".