Association of the Social Determinants of Health With Quality of Primary Care
Bibliographic record
Abstract
PURPOSE: In primary care, there is increasing recognition of the difficulty of treating patients' immediate health concerns when their overall well-being is shaped by underlying social determinants of health. We assessed the association of social complexity factors with the quality of care patients received in primary care settings. METHODS: Eleven social complexity factors were defined using administrative data on poverty, mental health, newcomer status, and justice system involvement from the Manitoba Population Research Data Repository. We measured the distribution of these factors among primary care patients who made at least 3 visits during 2010-2013 to clinicians in Manitoba, Canada. Using generalized linear mixed modeling, we measured 26 primary care indicators to compare the quality of care received by patients with 0 to 5 or more social complexity factors. RESULTS: Among 626,264 primary care patients, 54% were living with at least 1 social complexity factor, and 4% were living with 5 or more. Social complexity factors were strongly associated with poorer outcomes with respect to primary care indicators for prevention (eg, breast cancer screening; odds ratio [OR] = 0.77; 99% CI, 0.73-0.81), chronic disease management (eg, diabetes management; OR = 0.86; 99% CI, 0.79-0.92), geriatric care (eg, benzodiazepine prescriptions; OR = 1.63; 99% CI, 1.48-1.80), and use of health services (eg, ambulatory visits; OR = 1.09; 99% CI, 1.08-1.09). CONCLUSIONS: Linking health and social data demonstrates how social determinants are associated with primary care service provision. Our findings provide insight into the social needs of primary care populations, and may support the development of focused interventions to address social complexity in primary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".