Mindfulness, Self-Care, and Wellness in Social Work: Effects of Contemplative Training
Bibliographic record
Abstract
The demands placed on human service workers in supporting people through challenging circumstances can contribute to high levels of stress and burnout. Self-care practices implemented regularly may decrease the impact of the high levels of stress while also serving as strategies for coping during particularly stressful times. The interconnections between contemplative practices, including mindfulness, as coping and preventative strategies for self-care practice among human service workers are beginning to emerge. We used a multimethod study to examine the effectiveness of eight weeks of contemplative practice training in increasing self-care, awareness, and coping strategies for 12 human service workers. Paired t-tests conducted on pre- and post-training scores on the Perceived Stress Scale and the Mindfulness Attention and Awareness Scale showed that mindfulness was significantly increased and that stress significantly decreased over the intervention. Thematic analysis from participant journaling and a focus group discussion suggests that time, permission, and place for learning and practicing mindfulness-based activities are necessary. A meditative model is presented to illustrate how enhanced awareness through mindfulness practice can increase self-care which can, in turn, positively affect the service human service workers provide to their clients.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".