Effects of dialectical behavior therapy skills training on outcomes for mental health staff in a child and adolescent residential setting
Bibliographic record
Abstract
Training in Dialectical Behavior Therapy (DBT) skills coaching is desirable for staff in psychiatric settings, due to the efficacy of DBT in treating difficult patient populations. In such settings, training resources are typically limited, and staff turnover is high, necessitating brief training. This study evaluated the effects of a brief training in DBT skills coaching for nursing staff working in a child and adolescent psychiatric residential program. Nursing staff (n = 22) completed assessments of DBT skill knowledge, burnout, and stigma towards patients with borderline personality disorder (BPD) before and after a six-week DBT skills coaching training. Repeated measure ANOVAs were conducted to examine changes on all measures from pre- to post- treatment and hierarchical linear regressions to examine relationships between pre- training DBT knowledge, burnout, and BPD stigma and these same measures post-training. The brief DBT skill coaching training significantly increased DBT knowledge (p = .007) and decreased staff personal (p = .02) and work (p = .03) burnout and stigma towards BPD patients (p = .02). Burnout indices and BPD stigma were highly correlated at both time points (p < .001); however, while pre-training BPD stigma significantly predicted post-training client burnout (p = .04), pre-training burnout did not predict post-training BPD stigma. These findings suggest that brief training of psychiatric nursing staff in DBT skills and coaching techniques can result in significant benefits, including reduced staff burnout and stigma toward patients with BPD-related problems, and that reducing BPD stigma may particularly promote lower burnout.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".