Back to the future: Covid-19 and the recurring debate over social determinants of disease, and health
Bibliographic record
Abstract
Since the 1980s, a large literature has developed on the social determinants of health, primarily non-communicable diseases for which mortality and morbidity can be shown to change across a socioeconomic gradient. Primarily regional or national in focus, they are joined, today, with an increasing focus on international health and the effect of inequalities between nations effect disease generation and spread. Similar and earlier literatures first considered socioeconomic factors influencing disease incidence and intensity primarily at local and regional levels. One such literature was primarily "sanitarian," focusing on general infrastructure needs (safe water, for example) to create a beter health environment. A second, primarily nineteenth century literature focused on social inequalities and the epidemic diseases in specific populations. This paper seeks to review these separate foci and then combine them into a more comprehensive understanding of both the general and specific determinants of health and disease at local, national, and international scales of address. It notes that while disease dynamics have been long known that current literatures typically consider socioeconomic determinants at local, national, and global scales as a new phenomenon.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".