Pressing Healthcare Needs in a Windsor-based Shelter Health Initiative
Bibliographic record
Abstract
Background: Windsor Shelter Health (WSH) offers = on-site medical services at shelters and drop-in centres for people experiencing homelessness (PEH) in Windsor, Ontario. PEH face barriers to health, including competing demands that may outweigh their desire for healthcare, transportation barriers, and being lost to follow up. WSH was established to improve healthcare experiences and outcomes for PEH by enhancing access to care that addresses their needs and improves population health. Approach: A survey was developed, validated, and distributed to client-facing staff at shelters and drop-in centres to understand the current state of healthcare access for PEH in Windsor and identify unmet healthcare service needs of PEH in Windsor as understood by client-facing workers to enhance the WSH model. Results: Survey results (n = 60) indicated strong support (96.7%) for continuous, ongoing, and direct shelter health supports accessible to PEH. Services felt to be most important included access to physicians for primary care, psychiatric care, addictions medicine, access to harm reduction, wound care, and counselling. Delays in access to care were felt to be due to clients' fear and mistrust of the healthcare system, clients' concerns regarding stigma experienced, and a lack of transportation. Conclusion: Results of the project will serve as a baseline for the development of targeted programs and services to effectively support and improve health care access and outcomes for people experiencing homelessness in Windsor. This may serve as a model for similar jurisdictions on how to build a well-integrated shelter-health model.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".