Measuring Lessons Learned from Durham Region’s Community Hub Model During COVID-19: A Support Solution for Individuals Experiencing Homelessness and Other At-Risk Populations
Bibliographic record
Abstract
The 2019 Coronavirus (COVID-19) pandemic severely limited the availability of community resources within the Regional Municipality of Durham, Ontario, Canada. It disrupted the lives of persons experiencing homelessness and other vulnerable populations. To address the gaps in resources, community stakeholders developed two pilot community hubs to respond to the unmet health, housing, and support needs of those impacted. This research utilized a mixed-methods research design to determine the effectiveness of the community hubs in responding to the unmet needs of patrons utilizing the services and the scalability of the community hub model as a viable regional service approach. Surveys were administered in person with seventy-five community hub patrons. Fifteen direct service staff completed self-administered online surveys. Interviews were conducted with five community hub managerial staff and two subject-matter experts who collaborated with one of the community hubs. Results from the study showed that the needs of patrons were deep and entrenched and required a progressive, co-located, integrated health and social service response model. Staff described the services as critical and lifesaving for the patrons. The descriptive statistical analysis revealed that 93% of patrons indicated that services offered by the community hubs met their needs. The main challenge for the community hubs was the lack of core funding. Implications of this study include establishing a regional, evidence-informed, integrated system of care that addresses the healthcare, social service, and housing needs of populations experiencing homelessness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".