Exploring Undergraduate Nursing Students’ Experiences of Engaging With a Dialectical Behavior Therapy-Skills Group Intervention: Findings of a Thematic Analysis
Bibliographic record
Abstract
STUDY BACKGROUND: Nursing students often have high levels of stress leading to negative consequences for academic performance and overall well-being. Novel strategies are needed to help students manage stress. PURPOSE: To explore students' experiences with an evidence-based intervention-Dialectical Behavior Therapy-Skills Group (DBT-SG). METHODS: We conducted a mixed-methods study to pilot test a DBT-SG intervention, modified for use with undergraduate nursing students. Qualitative data collected as part of this study included focus groups and written responses on a study questionnaire. Thematic analysis of these data was undertaken to explore how students experienced the intervention. The results of this analysis are reported here. RESULTS: . CONCLUSIONS: Our findings suggest that by engaging with DBT-SG, nursing students felt accepted and validated, acquired a variety of skills to cope with stress, as well as developed new perspectives, such as the value of practicing self-care, which contributed to enhanced well-being. Future research could build on these results by further exploring how to best create accepting and validating learning environments where students are encouraged to develop interpersonal relationship skills and enact self-care to further support their well-being and professional development.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".