Understanding the role of networks in building capacity for systems change: A case study of two Canadian networks implementing Housing First
Bibliographic record
Abstract
Housing First is an evidence-based intervention designed to house individuals who are chronically homeless and are experiencing serious mental illness. The cross-sector collaboration required to provide person-centred supports to this population has resulted in increased understanding of Housing First as a whole systems response. Housing First implementation acts as a catalyst for systems change, yet research on how this change occurs is limited. This study examined the role of regional networks in advancing systems change through Housing First. A qualitative, multiple case study was conducted to examine two multi-city networks established by community leaders in the Canadian homelessness sector. Data collection activities included document analysis, interviews (n = 10), and two follow-up focus groups. Thematic analyses were conducted for each network, followed by a cross-case analysis. Findings indicate that engaging in a multi-city network increases leaders’ collective capacity to create conditions for change and to advance and sustain systems-level changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".