Healthy@Home, An Integrated Health and Social Care Program for a Marginalized Population Group in Toronto, Canada
Bibliographic record
Abstract
Introduction: Integrated health and social care (IHSC) can support improved health and social care outcomes for highly vulnerable and marginalized population groups. Toronto has one of the most diverse populations in the world. Immigrants account for 46% of the city's population. Russian-speaking Jewish older adults are a population group who are at significant risk of isolation and poor access to health and social services. In response an adult day program, Healthy@Home (H@H) was established through multi-organizational partnership among community-based health and social care outcomes led by social services agencies has spread to 20 sites across Canada. Methodology: a qualitative study of H@H was undertaken to explore the factors that support successful integrated services delivery for this high-risk population group. Key informant interviews and focus groups were completed involving eight health and social care agencies to explore the factors that support successful IHSC. Results: The qualitative study identified eleven critical factors that support IHSC among many health and social care agencies. They include strong communications, shared vision and goals, team-based care, dedicated resources, inter-organizational culture, and pre-existing relationships were overwhelmingly regarded as being key to the success of H@H partnerships. Leadership and accountability agreements were also regarded as being important factors. The H@H case also identified pre-existing relationships, role clarity and champions that contributed towards its integration success. The H@H case offers up some unique insights into how context can have an important role in influencing IHSC and that may be unique to services provision in Toronto. Ten contextual factors were identified as having exerted an influence on services partnership. They include aging population, immigration population, transportation, differences in the healthcare and social care sectors, accountability agreements with governing authorities and funders, public funding, governance authorities, sense of community among the providers, public policies and being part of the not-for-profit sector. Lessons Learned: The H@H study has shown that IHSC can occur voluntarily and at the grass-roots level. It also provides insights into how IHSC can be initiated and driven by social services agencies who are united by a shared vision and goal, and a strong sense of community. Despite limited funding and external resources, these organizational partners have been able to engage in IHSC for more than a decade in order to respond to a significant unmet need for a population group that was being ignored and marginalized due to their socio-economic status, their immigration status, and language barriers by existing health and welfare systems. Their isolation from mainstream society put them at significant risk of falling through the Canadian welfare system cracks. These community-based agencies, with close ties to their neighbourhoods were able to engage, respond, coordinate, and integrate flexibly. Contextual factors including a supportive policy environment that was aimed at supporting ageing in place, seniors care, poverty reduction and immigrants and a vibrant community of culturally competent providers has made H@H a success.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".