Working Together: Interdisciplinary Training within Live-In Treatment
Bibliographic record
Abstract
A strong and cohesive interdisciplinary support team is critical to the success of children’s mental health live-in treatment program. Kinark Child and Family Services, a leading child and youth mental health organization in Ontario, Canada, assessed the degree to which its community-based live-in treatment programs align with identified best practices and used the findings to inform the implementation of an interdisciplinary team that delivers a unified clinical approach to treatment and care. This paper reviews the results of the assessment, focusing specifically on interdisciplinary teamwork and the collaboration, consultation, and training that is crucial for staff working in live-in treatment programs. The benefits of this collaboration on the therapeutic milieu for complex children and youth cannot be overstated as clients are supported by multiple professionals throughout their treatment journey, including child and youth care practitioners, clinical therapists, psychologists, case managers, and nurses. Training in dialectical behavior therapy (DBT) is provided to all staff to ensure that all members of the patient’s interdisciplinary team offer a consistent approach in the delivery and support of individualized treatment plans. We contend that our training approach for interdisciplinary staff in our live-in treatment programs, including comprehensive training in DBT, is beneficial for clients and families. Consideration for future program evaluation and interdisciplinary training initiatives are presented.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".