Planning a Multi‐site, Complex Intervention for Homeless People with Mental Illness: The Relationships Between the National Team and Local Sites in Canada's At Home/Chez Soi Project
Bibliographic record
Abstract
This research focused on the relationships between a national team and five project sites across Canada in planning a complex, community intervention for homeless people with mental illness called At Home/Chez Soi, which is based on the Housing First model. The research addressed two questions: (a) what are the challenges in planning? and (b) what factors that helped or hindered moving project planning forward? Using qualitative methods, 149 national, provincial, and local stakeholders participated in key informant or focus group interviews. We found that planning entails not only intervention and research tasks, but also relational processes that occur within an ecology of time, local context, and values. More specifically, the relationships between the national team and the project sites can be conceptualized as a collaborative process in which national and local partners bring different agendas to the planning process and must therefore listen to, negotiate, discuss, and compromise with one another. A collaborative process that involves power-sharing and having project coordinators at each site helped to bridge the differences between these two stakeholder groups, to find common ground, and to accomplish planning tasks within a compressed time frame. While local context and culture pushed towards unique adaptations of Housing First, the principles of the Housing First model provided a foundation for a common approach across sites and interventions. The implications of the findings for future planning and research of multi-site, complex, community interventions are noted.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".