Is Multidimensional Treatment Foster Care (MTFC) More Effective at Reducing Externalizing Behaviours Exhibited by At-Risk Youth in Foster Care Than Group or Institutionalized Care?
Bibliographic record
Abstract
Abstract: This literature review seeks to highlight an evidence base for Multidimensional Treatment Foster Care (MTFC) as an alternative to group or institutionalized care for at-risk youth. A literature review was conducted and studies that used experimental designs were selected for analysis. Due to a lack of Canadian research, it is hard to know whether MTFC is more effective than group or institutional care in a Canadian context. Despite the implementation of an Indigenous rights-based legal framework to guide child welfare practices with First Nations, Inuit, and Métis (FNIM) children and youth, tens of thousands of Indigenous children and youths remain in foster, group, or institutionalized care in Canada and face significant challenges when “aging out” of the system. Indigenous child, youth, family, and community services remain underfunded, and vulnerable Indigenous youth lack access to evidence-based treatments, which results in placement or travel of youth outside of their communities to receive mental health services. Placement in an MTFC setting appears to have the benefit of helping to reduce externalizing behaviour, more than group or institutional placements. Further research is needed to understand the needs of Indigenous communities and youth, whether an MTFC model would be effective within FNIM cultural contexts in Canada, and if the model could be implemented within FNIM communities.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".