An interprofessional urban health elective focused on the social determinants of health
Bibliographic record
Abstract
BACKGROUND: More than half of the world's population now lives in cities. Health professionals should understand how social factors and processes in urban spaces determine individual and population health. We report on lessons from an interprofessional urban health elective developed to focus on the social determinants of health (SDOH). METHODS: An interprofessional committee developed an urban health elective based in downtown Toronto. Course objectives included promoting collaboration to address SDOH, identifying barriers to care, accessing community-based resources, and learning to advocate at individual- and community-levels. RESULTS: Seventeen students from eight disciplines participated during the 2011-2012 academic year. Sessions were co-facilitated with community partners and community members identified as experts based on their personal experience. Topics included housing, income and food security, Indigenous communities in urban spaces, and advocacy. Students collaborated on self-directed projects, which ranged from literature reviews to policy briefs for government. Students particularly valued learning about community agencies and hearing from people with lived experience. CONCLUSION: The specific health challenges faced in urban settings can benefit from an interprofessional approach informed by the experiences and needs of patient communities. This elective was innovative in engaging students in interprofessional learning on how health and social agencies collaborate to tackle social determinants in urban spaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".