Neighbourhood Care Teams: Integrating Health Care and Social Services for Seniors in Toronto Community Housing
Bibliographic record
Abstract
Toronto Seniors Housing Corporation (TSHC), owned by the City of Toronto, provides housing for 15,000 low-income seniors in 83 seniors-designated buildings across Toronto. The North Toronto Ontario Health Team (including primary care, hospital, community and home-care) partnered with one of the TSHC buildings in North Toronto to develop and implement a Neighbourhood Care Team (NCT) model to support TSHC’s Integrated Service Model to address tenants' health and social needs, co-designed with the tenants. The goal of the NCT is to provide an integrated model of care that is accountable to meeting the needs of people living within a specific neighbourhood so that people experience one system that provides simple access to service, and care that is coordinated with streamlined communication of health care providers. The NCT objectives include: Increasing primary care provider connections Increasing mental health & addictions care access and support options Increase Digital Health access and literacy to support primary care and specialist access, reduce social isolation and increase wellness Reduce avoidable ED and hospital use. The service design is guided by a co-design process with the tenants as follows: Door to door survey to engage tenants in identifying their barriers and the services and supports most meaningful to them Eliciting and voting on key education and support initiatives at an influenza vaccination clinic Communication back to tenants regarding the results of the survey and how the strategies/activities planned for the building have been prioritized based on their feedback. Multi-organization Education Fair focusing on the top issues addressed during the vaccination clinic survey which was well attended Regular educational sessions in response to tenant interest, combined with a self-screening component to help link the information to a concrete service/intervention to promote better health. Providing translation support to enable access and engagement by tenants from a variety of cultural backgrounds. Ongoing commitment to continue and co-design services and elicit tenants’ feedback. The team has worked to design structures to strengthen coordination and collaboration among the various delivery partners: Multi-organizational bi-weekly huddles to discuss residents identified with unmet needs (with consent or anonymized without consent) and identify options for improving their access to health care/social services and respond to their needs in a timely manner Designed pathways for ensuring attachment to primary care, access to primary care and specialist support, access to home care services and assistance with social determinants of health Established mechanisms to obtain informed consent and enable information sharing between delivery partners. Multi-modality tenant engagement to tailor services and supports to a TSHC building has led to increased involvement by tenants and a growing interest by tenants in strategies to improve their health and social inclusion. Cross-sector collaboration is an efficient and effective way to establish needs-based integration of health and social care services in this setting. Strong leadership as well as co-developed processes, frequent building meetings and cross-sector huddles were effective ways of sharing innovative ways of meeting needs with limited resources.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".