Bibliographic record
Abstract
The UHN Gattuso Centre for Social Medicine recognizes that health and poverty are inextricably linked. Based at the largest academic health sciences network in Toronto, University Health Network, (UHN), the Centre partners with community organizations and people with lived experience to integrate social determinants of health into care delivery. UHN patients with repeat visits to emergency departments or readmitted to the hospital multiple times (“super-utilizers”) are referred for high-quality wrap around care addressing the intersection of medical and social issues including food insecurity, homelessness, social isolation, substance use, and mental health challenges. Health disparities across populations continue to be seen in Canada, despite a universal health care system. These inequities produce avoidable adverse health outcomes and significantly impact health systems. Social Medicine uses a population health approach to provide integrated, patient-centered, team-based care to marginalized patients that have worse health outcomes compounded by the social determinants of health. In Canada, 65% of hospital and home-care costs are attributed to high-cost users, who account for disproportionate healthcare costs. At UHN, 50 patients made up over 2,000 emergency department (ED) visits in 2023, accounting for an approximate 21% of all visits. Having no fixed address, or having visits related to homelessness, mental health, or substance use were associated with a greater number of ED visits. This link between health and social outcomes demonstrates the importance of co-designing programs with people with lived experience, incorporating local knowledge from community partners and forging relationships with all levels of government, to improve population health. Working closely with the Lived Experience Advisory Council, a dyad model of Community Health Workers (CHWs) and Nurse Practitioners (NPs) was co-created to provide mobile primary care, navigation and accompaniment, case management, harm reduction, and links to community services. Currently there are three pillars to this program: Peer Support Workers in the Emergency Department and a Stabilization and Connection Centre (SCC) for underhoused patients with alcohol intoxication or drug overdose who would otherwise present at emergency departments without acute medical concerns A Social Medicine Housing initiative providing 51 units of permanent supportive housing with integrated health and social supports for patients with medical and social complexity An integrated health and social care model for underserved populations who are at risk for repeated emergency visits and hospital readmissions. To date the Peer Workers in the Emergency Department and SCC are the most developed models (nearly 5,000 patient interactions) and have shown important outcomes. The SCC reduces EMS offload times from 5-7 hours to 6 minutes, diverting nearly 2,000 patients from UHN EDs. The intention is to continue to build these initiatives and further integrate the housing and health care models. Participants will learn the fundamentals: To engage with people with lived experience, service providers, and other stakeholders throughout the design, implementation, and evaluation stages To establish partnerships with community organizations that model trauma-informed, culturally sensitive comprehensive care To identify best practices for delivering integrated care and improving population health To identify high priority populations To generate partnerships with government and philanthropic donors
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".