Racial and Ethnic Differences in the Protective Effect of Educational Attainment on Chronic Pain
Bibliographic record
Abstract
Background: The broad scientific community generally associates high socioeconomic status (SES) with better health. However, the protective effects of high educational attainment on health may be weaker for racial and ethnic minorities than non-Latino White individuals. It is important to study whether this difference holds for chronic pain among Black and Latino individuals. Objectives: To compare the association between educational attainment and chronic pain in the US, considering the racial and ethnic background of individuals. Methods: The current study used baseline data from the Population Assessment of Tobacco and Health (PATH-Adults) study. All participants were 18+years old. A total number of 28204 Non-Latino, Latino, White, and Black individuals were enrolled. The outcome was chronic pain treated as a continuous measure. The predictor was educational attainment. Moderators were race and ethnicity. Results: Our linear regressions in the pooled sample showed that higher educational attainment was associated with a lower level of chronic pain; however, this association was weaker for Latinos and Blacks compared to non-Latino and White individuals. Our stratified models also showed that higher educational attainment was more consistently associated with a lower level of chronic pain for non-Latino White individuals than racial and ethnic minorities. Conclusion: The presumed protective effect of educational attainment against chronic pain among individuals varies between different racial and ethnic groups. Future research should test the role of stressful jobs and working conditions in weakening the protective effects of SES against chronic pain for Blacks and Latinos compared to non-Latino White individuals.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".