Canadian Wraparound: Measuring Implementation Fidelity for Youth in Care
Bibliographic record
Abstract
For youth in the care of child welfare, the need for a greater emphasis on service integration and youth-centered approaches is widely agreed. This case study examined an implementation of the Wraparound approach, which focuses on coordinated service delivery and youth-centred care, as delivered by an intervention team at a Canadian urban community health centre with 12 youths in care. Wraparound is a philosophy of care and a process that facilitates the provision of integrated support for individuals with complex needs. The high-fidelity implementation of this approach has been identified as critical to improvements in life outcomes. The Wraparound Fidelity Index Short Version (WFI-EZ), which measures adherence to the guiding principles and primary activities of Wraparound was administered with team members, facilitators, caregivers, and a youth. The Team Observation Measure (TOM-2), which measures facilitation skills and team work as observed during Wraparound meetings, was administered with a sample of four Wraparound teams. Overall fidelity scores at this site, though below average, are encouraging considering the complex profiles and multi-system needs of the youths. Intensive training and ongoing coaching of Wraparound facilitators contributed to high ratings, while success was limited by narrow definitions of family and by system-level constraints such as high turnover among social workers. Future research should explore the value of peer support for youth and caregivers, training for all team members in the Wraparound approach, and adapting the fidelity assessment tools to better account for family, community, and system-level constraints.
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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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".