Bringing it All Together: Integrating Services to Address Homelessness
Bibliographic record
Abstract
Homeless-serving systems are striving for greater impact and efficiency amidst austerity. Those with lived experience, governments, funders, and service providers have consistently and increasingly called for enhanced integration across services, organizations, systems, processes and policies as a means of maximizing existing resources. Here, the discourse of integration refers to ways of working better together across diverse internal and external organizational boundaries. At the heart of these efforts lies the assumption that integration is the desired state, an ideal that will result in improved outcomes at individual and population levels. As actors actively engaged in homeless-serving system planning work, we draw from on-the-ground experiences in Calgary, Alberta to consider how a nuanced understanding of integration can shape the evolution of a systems planning response to homelessness. Our practical experience working in the homeless-serving system on diverse integration initiatives has prompted us to reconsider the evidence and critically probe the implicit link between systems integration efforts and improved individual and population outcomes. In some instances, we have experienced how integration efforts can result in the addition of new layers of complexity to the issue they were created to resolve in the first place. In other cases, integration efforts delivered marginal benefits given the amount of resources used as inputs into planning and implementation. Of course, there are also examples where integration spurs positive impacts at the system and target population levels. Rather than declaring a course of action, this paper is intended to spur discussion. We are posing questions we continue to grapple with at a praxis level, which may resonate with fellow policy makers, system planner organizations, service providers and researchers. We want to go beyond conventional wisdom to probe the merits of integration as a de facto answer to complex social issues by asking: What exactly is integration? When and how is integration appropriate? What are the pitfalls of integration and how do we manage risks? Findings suggest that while ventures to enhance integration remain important, they are not and cannot be the panacea to social challenges, including homelessness. If integration is an appropriate response, we must understand what makes it work, what environment it thrives in, and what success looks like. We maintain that no matter how system integration is implemented or delivered, a central focus on the person served must be maintained, rather than systems themselves.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.005 | 0.009 |
| 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".