Framework for building primary care capacity to address the social determinants of health.
Bibliographic record
Abstract
PROBLEM ADDRESSED: Family physicians have long understood that social factors influence the health of individuals and communities; however, most primary care organizations have yet to develop the capacity to specifically address these social determinants of health (SDOH). OBJECTIVE OF PROGRAM: To support SDOH interventions and foster an organizational culture in which addressing SDOH is considered part of high-quality primary care. PROGRAM DESCRIPTION: An academic family health team in Toronto, Ont, established a committee comprising a diverse group of health professionals focused on the SDOH. The committee analyzes how social factors affect patients and supports the development and implementation of interventions. The committee's current interventions include the following: collecting and analyzing detailed sociodemographic data to identify health inequities; launching an income security health promotion service; establishing a medical-legal partnership; implementing a child literacy program in its clinics; and developing an advocacy and service program to improve access to decent work. Each intervention includes a rigorous evaluation plan to assess implementation and effect. Next steps include developing tools to enable organizations to "move upstream" and adopt a health equity approach to all work, including joining in advocacy. CONCLUSION: Primary care providers are well situated to address SDOH. This article provides a framework that can assist every large primary care organization in establishing a similar committee dedicated to SDOH, which could help build a network across Canada to share lessons learned and support joint advocacy.
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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 | 0.032 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".