Essential Conditions for Partnership Collaboration within a School-Community Model of Wraparound Support
Bibliographic record
Abstract
Children and youth often face barriers that hinder their ability to engage in school, such as poverty, family challenges, and maltreatment. For this reason, children require additional supports if they are to be set up for success in school and life. Collaborative school-community models of wraparound support have been demonstrated as effective approaches for supporting vulnerable children and families to foster positive outcomes. Such models rely on collaborative partnerships between schools and community agencies to coordinate services for children and families. Accordingly, there is a need to understand factors that influence this collaboration in school settings. This study explores partnership collaboration between school and community partners through the case of All in for Youth, a school-based wraparound model of support in western Canada. Focus groups of n = 79 partners across eight schools were analysed, guided by qualitative description methodology. Five essential conditions were identified for partnership collaboration, including value-based training, mutual recognition of expertise, school leadership, established and flexible communication channels, and appropriate staff resources. These conditions can be used to help inform the implementation of similar school-community models of support to foster collaborative partner processes and promote positive outcomes among children, youth, and families.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| 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".