Aligning Method with Theory: A Comparison of Two Approaches to Modeling the Social Determinants of Health
Bibliographic record
Abstract
There is increasing interest in the study of the social determinants of maternal and child health. While there has been growth in the theory and empirical evidence about social determinants, less attention has been paid to the kind of modeling that should be used to understand the impact of social exposures on well-being. We analyzed data from the nationwide 2006 Canadian Maternity Experiences Survey to compare the pervasive disease-specific model to a model that captures the generalized health impact (GHI) of social exposures, namely low socioeconomic position. The GHI model uses a composite of adverse conditions that stem from low socioeconomic position: adverse birth outcomes, postpartum depression, severe abuse, stressful life events, and hospitalization during pregnancy. Adjusted prevalence ratios and 95% confidence intervals from disease-specific models for low income (<20,000/year) compared to high income (≥ 80,000/year) ranged from a low of 1.43 (1.09-1.85) for adverse birth outcomes to a high of 5.69 (3.59-8.84) for stressful life events. Estimates from the GHI model for experiencing three to five conditions yielded a prevalence ratio of 18.72 (9.29-35.77) and a total population attributable fraction of 78%. While disease-specific models are important for uncovering etiological factors for specific conditions, models that capture GHIs might be an attractive alternative when the focus of interest is on measuring and understanding the myriad consequences of adverse social determinants of health.
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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 | 0.058 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".