Engaging Street-Involved Youth in Dialectical Behaviour Therapy: A Secondary Analysis.
Bibliographic record
Abstract
OBJECTIVE: The objective of this secondary analysis was to identify factors associated with engagement of street-involved youth in a Dialectical Behavioural Therapy (DBT) intervention. METHODS: This was a cross-sectional correlational study. Youth were recruited from two agencies providing services to street-involved youth in Canada. Mental health indicators were selected for this secondary analysis to gain a better understanding of characteristics that may account for levels of engagement. RESULTS: Three distinct groups of participants were identified in the data, a) youth who expressed intention to engage, but did not start DBT (n=16); b) youth who started DBT but subsequently dropped out (n=39); and c) youth who completed the DBT intervention (n=67). Youth who did engage in the DBT intervention demonstrated increased years of education; increased depressive symptoms and suicidality; and lower levels of resilience and self-esteem compared to youth participants who did not engage in the intervention. CONCLUSIONS: These findings indicate that it is possible to engage street-involved youth in a DBT intervention who exhibit a high degree of mental health challenges. Despite the growing literature describing the difficult psychological and interpersonal circumstances of street-involved youth, there remains limited research regarding the process of engaging these youth in service.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".