Collaborate for a multidisciplinary and intersectoral vision of homelessness: a case study of local initiatives in Quebec
Bibliographic record
Abstract
In the early 200s, several multidisciplinary initiatives emerged in Quebec as part of a broader reflection on how to address homelessness in an intersectoral manner. While the literature provides analyses and descriptions of various initiatives aimed at tackling homelessness and their effects, there is little research on the mechanisms and processes by which multi-level, cross-sector initiatives collaborate. Even less research addresses the initial conditions that may or may not favor such collaboration. Yet homelessness, with its multiple causes and the complex needs of those experiencing it, has prompted inter-professional and intersectoral responses that should be studied to understand the implementation of local policies.At the level of public action, housing, social and health services, employment, etc., are all involved. But how does this translate into the implementation of local policies? What are the challenges for local stakeholders in combating homelessness at the clinical, community, and strategic levels? What are the effects and adaptations since COVID-9?To answer these questions, we will focus on concrete cases in several Quebec municipalities: local initiatives involving intersectoral and collaborative governance arrangements from clinical, integrated care, social, community, and other perspectives for people experiencing homelessness. We will examine the factors that enable these initiatives to take root and develop, as well as the constraints and tensions involved in implementing these intersectoral or collaborative arrangements, such as funding methods, expertise, legitimization processes, and political windows of opportunity. This presentation is based on research conducted since 207 by Lara Maillet's team. It relies particularly on a developmental evaluation with the directly concerned clinical and community environments (research partners) and on some forty interviews and participant observations. The goal of the presentation is to present and discuss the results with the communities of practice attending the conference and to identify potential avenues for scaling up in other areas in the interest of equity and social justice.The results show that the cases studied provide contrasting examples of models at the intersection of community and intersectoral responses, and integrated inter-organizational team responses. Above all, the research highlights the local determinants that influenced the implementation of the initiatives and the adaptations that led to the development of two distinct modes of governance. Temporality, funding types, power relationships between organizations, and the management of interdependencies between players are central to understanding the dynamics. These elements allow us to reflect on the coherence of adaptive systems, resilience, and sustainability.We believe that a strong, autonomous model rooted in intersectorality provides a more robust and resilient response to the changing needs of the population. These questions are even more pertinent in a period of crisis such as the COVID-9 pandemic, when adaptation and collaboration were essential.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".