Family and Natural Supports: A Framework to Enhance Young People’s Network of Support
Bibliographic record
Abstract
This framework introduces and provides an overview of Family and Natural Supports (FNS), a preventive approach to addressing youth homelessness. FNS is a key component of a larger systemic shift in responses to homelessness, away from emergency service provision and instead toward the prevention of youth homelessness. This framework explains FNS, its core principles and guiding philosophy, presents considerations for implementing FNS in communities, and provides case examples of what this work can look like in practice. It also addresses the need for early interventions (including Family and Natural Supports) and the compelling reasons to shift to prevention as the new prevailing response to youth homelessness. \n \nThe FNS framework builds on the foundational work of the Change Collective’s Working with Vulnerable Youth to Enhance their Natural Supports. The FNS framework was co-developed with practitioners from the Making the Shift Demonstration (MtS DEMs) sites in Toronto, Calgary, Edmonton, Fort McMurray, Grande Prairie, Lethbridge, Medicine Hat, and Red Deer. The framework is also informed by preliminary qualitative data and lessons learned from the eight demonstration projects in Ontario and Alberta that are testing the FNS principles laid out here. This guide will be updated based on ongoing research emerging from these projects, including developmental, implementation, and summative evaluations.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".