A BRIEF MINDFULNESS EDUCATION PROGRAM FOR SOCIAL WORK STUDENTS
Bibliographic record
Abstract
Mindfulness has recently been a notably hot topic for social work discipline. Many social work academics are increasingly considering integrating these programs into the social work curriculum; however, many see such programs time-consuming and difficult to integrate into social work crowded curriculum, consequently proposing shorter versions. This doctoral thesis aimed to design a brief mindfulness-based education program for social work students and to examine its effectiveness. The study used a quantitative approach, with a quasi-experimental pre-test post-test study design, to examine the program's effectiveness in two separate modules of traditional classroom-based and online programs. The study's participants were 101 third-year undergraduate social work students studying at Hacettepe University. Thirty-two and 27 students respectively comprised the intervention and waitlist control groups of the traditional classroom-based mindfulness module, and 23 and 19 students the online module. A personal questionnaire, Depression, Anxiety and Stress Scale-21 (DASS-21), Self-Compassion Scale (SCS), Ruminative Thought Style Scale (RTS), Mindful Attention Awareness Scale (MAAS), and The Toronto Empathy Questionnaire (TEQ) were utilized on the intervention and waitlist control groups before and after the intervention. The Analysis of Covariance (ANCOVA) was applied to examine whether the intervention groups reported better results on each variable regarding the measures, after the brief mindfulness intervention. Moreover, multiple mediation analyses were utilized to find out about the possible working mechanisms of the intervention. The results indicated that the brief mindfulness program, in both modules similarly, could make statistically significant improvements in the students' psychological health; however, not in their therapeutic relationship. Mediation analyses showed that adjusting trait mindfulness, rumination, and self-compassion were the possible working mechanisms of the program. Therefore, this brief mindfulness program can be used as an effective and introductory mindfulness program for undergraduate social work students in either classroom-based or online modules; nevertheless, more research is recommended.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".