Bibliographic record
Abstract
Addressing the mental health needs of children and youth is a priority. One way to provide support for children and youth with severe mental health needs is through the implementation of the wraparound approach in school-based settings. This study explored the fidelity of implementation of the wraparound approach for two youth with severe mental health needs in two rural schools in Manitoba, Canada. Perspectives from key stakeholders on wraparound teams were obtained using the Wraparound Fidelity Index (WFI-EZ). Wraparound team meetings also were observed using the Team Observation Measure (TOM-2). Results indicated that the school-based wraparound teams, led by trained wraparound facilitators, showed adherence to most elements of the wraparound practice model. Areas, which demonstrated the highest degree of fidelity, included the provision of needs-based and strength-based support. In one school, lower levels of fidelity were found with respect to the presence of natural supports and the caregiver’s perceptions of teamwork. Identifying the areas of relative strength and weakness in the implementation of school-based wraparound may help to guide future program planning and training in wraparound implementation, and may highlight the capacity of schools in Manitoba to lead the provision of this intensive interdisciplinary support.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".