A Framework for Developing an Integrated Shelter Health Model in a Mid-Sized Community: The Windsor Shelter Health Experience
Bibliographic record
Abstract
Windsor Shelter Health is an integrated program that offers comprehensive on-site medical services at shelters and drop-in centres for people experiencing homelessness in Windsor, ON. Although homelessness exists in most Canadian communities, there is more understanding of homelessness responses in larger urban centres. Windsor is a mid-sized border city in southwestern Ontario. Here, we explore the structures and processes that have been used to build a shelter health model in a city of this size and some early outcomes. Examples of these include a thoughtful governance structure, a shared electronic medical record, co-location of services, embedded research and educational programs, sustainable funding sources and collaboration between partners. Using this model, we were able to divert patient visits from the emergency department to be better managed in an outpatient setting, increase patient attachment to primary care and create novel avenues for education for both learners and staff in Windsor. Therefore, we demonstrate why these are essential components of this new program, and how other mid- and smaller-sized cities might incorporate these elements into their own shelter health programs.
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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 | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.030 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".