Evaluation of Mindfulness-Based Cognitive Therapy to Reduce Psychological Distress and to Promote Well-Being
Bibliographic record
Abstract
The objective of this study was to assess the effectiveness of an 8-week mindfulness-based cognitive therapy (MBCT) group intervention to reduce psychological distress as well as to strengthen self-esteem, resilience, and general well-being for individuals living with a chronic illness and/or other health challenges. This pilot study employed a pre–posttest design to evaluate the outcome of the intervention for individuals receiving care in a Community and Family Medicine department and a Women’s Health Centre located in an urban downtown hospital. A total of 42 patients were enrolled in the study and 35 participants completed the intervention. Thirty-five of 42 enrolled participants completed the intervention. Twenty-eight participants completed the pretest and posttest assessments, which demonstrated statistically significant improvement on most outcome measures, including the Center for Epidemiological Studies–Depression Rating Scale (CES-D), Depression Anxiety Stress Scale (DASS), Affect Balance Scale (ABS), the Resilience Scale (RS), and the Five-Item World Health Organization Well-Being Index (WHO-5). Large effect sizes were observed for the CES-D, the ABS, and the DASS Stress subscale. Observed improvements were sustained at 4 weeks posttreatment. Results are consistent with studies indicating that MBCT offers promise in alleviating psychological distress for those who are seeking care in primary health care settings. Continued focus on how to best implement MBCT in primary health care, particularly for those with comorbid physical and mental health conditions, supports the World Health Organization’s recommendation to integrate evidence-based mental health care within primary health care to promote equitable access to care for those most in need.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".