Collaboration and involvement of persons with lived experience in planning Canada's At Home/Chez Soi project
Bibliographic record
Abstract
Planning the implementation of evidence-based mental health services entails commitment to both rigour and community relevance, which entails navigating the challenges of collaboration between professionals and community members in a planning environment which is neither 'top-down' nor 'bottom-up'. This research focused on collaboration among different stakeholders (e.g. researchers, service-providers, persons with lived experience [PWLE]) at five project sites across Canada in the planning of At Home/Chez Soi, a Housing First initiative for homeless people with mental health problems. The research addressed the question of what strategies worked well or less well in achieving successful collaboration, given the opportunities and challenges within this complex 'hybrid' planning environment. Using qualitative methods, 131 local stakeholders participated in key informant or focus group interviews between October 2009 and February 2010. Site researchers identified themes in the data, using the constant comparative method. Strategies that enhanced collaboration included the development of a common vision, values and purpose around the Housing First approach, developing a sense of belonging and commitment among stakeholders, bridging strategies employed by Site Co-ordinators and multiple strategies to engage PWLE. At the same time, a tight timeline, initial tensions, questions and resistance regarding project and research parameters, and lack of experience in engaging PWLE challenged collaboration. In a hybrid planning environment, clear communication and specific strategies are required that flow from an understanding that the process is neither fully participatory nor expert-driven, but rather a hybrid of both.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".