Improve service coordination and delivery in community hubs serving homeless and at-risk populations
Bibliographic record
Abstract
Abstract Background Two community hubs are currently located in Durham Region, Ontario, Canada, to provide a single point of access to a wide range of support services for individuals experiencing homelessness and other at-risk populations. The community hub in Oshawa is formally known as the Back Door Mission for the Relief of Poverty and the community hub in Ajax is formally known as the Ajax Hygiene Hub. It is unclear if these two community hubs are effective in addressing the needs of individuals experiencing homelessness and how the COVID-19 pandemic continues to impact these services amongst this population. This study was conducted to identify gaps and barriers within the community hub models as well as provide recommendations to improve the coordination and delivery of services serving individuals experiencing homelessness and other at-risk populations. Methods A mixed methods approach was utilized in this study, which included surveys for individuals experiencing homelessness, through open-ended and close-ended questions to assess their experiences at either one of the two community hubs. A total of 75 surveys were completed by the study participants (40 surveys in Oshawa and 35 in Ajax). Thematic analysis was performed for all the open-ended survey responses. A literature review was also conducted to evaluate the community hub models as well as best practices for the implementation locally, nationally, and internationally. Results Data analysis for the open-ended survey responses revealed the need for housing support, increased resources for medical services, and the expansion of programs provided by the community hubs. Conclusions Homelessness is a major public health issue however community hubs play a pivotal role in addressing this concern in Durham Region. The equitable access to a diverse range of services that are co-located in a community hub is imperative for individuals experiencing homelessness, especially during the COVID-19 pandemic. Key messages
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".