Wraparound in Practice: A Program Description of a School-Based Wraparound Model of Support for Children and Their Families in Canada
Bibliographic record
Abstract
Abstract Over the past few decades, wraparound models of support have received significant attention as an effective and efficient way to provide social support to individuals with diverse needs. This article presents a program description of the All in for Youth (AIFY) initiative, which is a collaborative school–community model of wraparound support implemented in eight schools in Edmonton, Canada. The AIFY model is led by a partnership between community organizations, school districts, and funders. This article describes how the school-based wraparound model is implemented in the Canadian school context, including its core components and processes. The authors also discuss the key facilitators and barriers to the successful implementation of the model, such as funding and resources, strong partner relationships and collaboration, and the ability to be adaptable. They emphasize the importance of interdisciplinary collaboration and highlight the need for ongoing investment and commitment to ensure the effectiveness and sustainability of wraparound models of support in schools. Overall, this article offers valuable insights into the practical implementation of a school-based wraparound model of support for children and their families in Canada.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".