Measuring Lessons Learned from Durham Community Hub Model during COVID-19: A Support Solution for the Homeless and other Vulnerable Populations
Bibliographic record
Abstract
Introduction/background: The COVID-19 pandemic significantly impacted the availability of community support and resources to vulnerable populations, including persons experiencing homeless within the Regional Municipality of Durham, Ontario, Canada. In response, the Region developed pilot community hubs to address escalating unmet housing, healthcare and support needs. Why did you do it? The community hub evaluation project is a community-based research co-created by a research team from Ontario Tech University and community stakeholders to measure the community hubs' effectiveness in addressing service users' unmet needs during the COVID-19 pandemic. Who is it for? The project is relevant to healthcare and social service policymakers and service providers, service users, researchers, and community organizers involved in researching, funding, designing and delivering healthcare and social services for vulnerable populations. Who did you involve and engage with? The project collaborators comprised a research team from Ontario Tech University and an advisory committee of community hub staff members and service users convened by the research team. All aspects of the project’s design and implementation, including creating the data collection tools, participant recruitment, data collection, data analysis and developing the knowledge translation products, were completed in collaboration with the research advisory committee that met monthly. Service users on the advisory committee were compensated for their time and contribution. What did you do? A mixed-methods research design was used to explore the experiences of the community hub service users, staff members and subject matter experts. Service users were recruited from the community hubs. Staff members were recruited through virtual staff meetings. Face-to-face surveys were administered to 75 service users at the community hubs. Staff online surveys were self-administered by 15 direct service staff. Virtual interviews were conducted with five community hub managerial staff and two subject matter experts. Quantitative data were calculated using the SPSS statistical software. Qualitative data were analyzed using thematic analysis. What results did you get? What impact did you have? The integrated service model was described as saving service users’ lives. Adequate, core funding is required for the sustainability of the community hubs. Results from the evaluation provided the regional government with data to help inform its strategic priorities for vulnerable populations. What is the learning for the international audience? The needs of vulnerable populations including persons experiencing homelessness are deep and entrenched. Community hubs integrate a range of healthcare and social support services, including the social determinants of health that respond to service users’ immediate and emerging needs. What are the next steps? Knowledge translation through conferences, symposiums, presentations at regional sector tables and peer-reviewed publications. Collaborating with the community hubs to utilize the research findings to support improvement in future service design and delivery.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".