Title: Socioeconomic History & Preventable Disease: A Comparative Analysis of Fundamental Cause Theory
Bibliographic record
Abstract
Abstract: Fundamental cause theory suggests that because persons of higher socioeconomic status have a range of resources that benefit health, they hold an advantage in warding off whatever particular threats to health exist at a given time. Therefore as risk factors that stratify health are eliminated, socioeconomic disparities in health remain. Accordingly, SES should be more strongly associated with diseases that are more preventable than with less preventable diseases, and SES should have a stronger relationship to health in countries where high economic inequality and no universal health insurance leads to greater competition for resources. Using longitudinal data from Canada (National Population Health Study) and the U.S. (Panel Study of Income Dynamics), trajectories of socioeconomic status are identified using latent class analysis and used to predict the odds of experiencing a highly preventable disease compared to a less preventable disease. Preliminary findings indicate that a history of low income increases one’s odds of experiencing a highly preventable disease in the U.S., but not in Canada. This suggests that social policies and level of economic inequality may buffer the relationship between socioeconomic resources and the incidence of preventable disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".