The Development of Regional Networks to Promote Housing First Implementation in CanadaThe Development of Regional Networks to Promote Housing First Implementation in Canada
Bibliographic record
Abstract
While knowledge mobilization strategies, such as training and technical assistance, have been used to facilitate the implementation of evidence-based practices, little is known about the role of networks in influencing implementation. In this article, we describe the role of a variety of networking strategies (regional training events, community of practice teleconference calls, and the creation of regional networks) used to implement Housing First (HF) in Canada during a three-year training and technical assistance initiative. We report on three main findings from research on this initiative. First, data from regional training events (n=110) revealed that 92% of participants wanted a regional HF network. Participants in the regional training events, as well as those who participated HF training needs assessment focus groups (k=11, n=83), believed that the networks should focus on mutual learning and influencing policy and have strong leadership and an open membership. Second, HF training events held in four regions of Canada (the West, the Prairies and northern territories, Ontario, and the Atlantic region) were very positively evaluated by participants (n=276) in terms of their value in increasing HF knowledge and comprehension, and overall satisfaction with the training. Third, field notes (n=146) were used to generate a narrative of HF networks that emerged from training and technical assistance activities, including a province-wide network in Ontario and practitioner-led networks in southwestern Ontario and British Columbia. We discuss how these network activities contributed to capacity-building in HF programs designed to end homelessness in Canada.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".