The Social Determinants of Health and Pandemic H1N1 2009 Influenza Severity
Bibliographic record
Abstract
OBJECTIVES: We explored the effects of social determinants of health on pandemic H1N1 2009 influenza severity and the role of clinical risk factors in mediating such associations. METHODS: We used multivariate logistic regression with generalized estimating equations to examine the associations between individual- and ecological-level social determinants of health and hospitalization for pandemic H1N1 2009 illness in a case-control study in Ontario, Canada. RESULTS: During the first pandemic phase (April 23-July 20, 2009), hospitalization was associated with having a high school education or less and living in a neighborhood with high material or total deprivation. We also observed the association with education in the second phase (August 1-November 6, 2009). Clinical risk factors for severe pandemic H1N1 2009 illness mediated approximately 39% of the observed association. CONCLUSIONS: The main clinical risk factors for severe pandemic H1N1 2009 illness explain only a portion of the associations observed between social determinants of health and hospitalization, suggesting that the means by which the social determinants of health affect pandemic H1N1 2009 outcomes extend beyond clinically recognized risk factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".