Building a Foundation to Reduce Health Inequities: Routine Collection of Sociodemographic Data in Primary Care
Bibliographic record
Abstract
INTRODUCTION: Detailed data on social determinants of health can facilitate the identification of inequities in access to health care. We report on a sociodemographic data collection tool used in a family medicine clinic. METHODS: Four major health organizations in Toronto collaborated to identify a set of 14 questions that covered a range of social determinants of health. These were translated into 13 languages. This survey was self-administered using an electronic tablet to a convenience sample of 407 patients in the waiting room of a primary care clinic. Data were uploaded directly to the electronic medical record. RESULTS: The rate of valid responses provided for each question was high, ranging from 84% to 100%. The questions with the highest number of patients selecting "do not know" and "prefer not to answer" pertained to disabilities and income. Patients reported finding the process acceptable. In subsequent implementation across 5 clinics, 10,536 patients have been surveyed; only 724 (6.9%) declined to participate. CONCLUSION: Collecting data on social determinants of health through a self-administered survey, and linking them to a patient's chart, is feasible and acceptable. A modified survey is now administered to all patients. Such data are already being used to identify health inequities, develop novel interventions, and evaluate their impact on health outcomes.
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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 | 0.048 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".