Training frontline community agency staff in dialectical behaviour therapy: building capacity to meet the mental health needs of street-involved youth
Bibliographic record
Abstract
Purpose The purpose of this paper is to evaluate the effectiveness of the dialectical behavior therapy (DBT) training which was provided to community agency staff (N=18) implementing DBT in the community with street-involved youth. Design/methodology/approach Staff participated in a multi-component approach to training which consisted of webinars, online training, self-study manuals, and ongoing peer consultation. To evaluate assess the effectiveness of the training, questionnaires assessing evaluating DBT skills knowledge, behavioral anticipation and confidence, and DBT skills use, were completed at baseline, immediately post-training, four to six months post-training, and 12-16 months post-training. Additionally, the mental health outcomes for youth receiving the DBT intervention are reported to support the effectiveness of the training outcomes. Findings Results demonstrate that the DBT skills, knowledge, and confidence of community agency staff improved significantly from pre to post-training and that knowledge and confidence were sustained over time. Additionally, the training was clinically effective as demonstrated by the significant improvement in mental health outcomes for street-involved youth participating in the intervention. Practical implications Findings suggest that this evidence-based intervention can be taught to a range of staff working in community service agencies providing care to street-involved youth and that the intervention can be delivered effectively. Originality/value These findings help to close the knowledge-practice gap between evidence-based treatment (EBT) research and practice while promoting the implementation of EBT in the community to enhance positive youth outcomes.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".