Socioeconomic Inequalities in Injury: Critical Issues in Design and Analysis
Bibliographic record
Abstract
Injuries continue to place a tremendous burden on the public's health and rates vary widely among different groups in the population. Increasing attention has recently been given to the effects of socioeconomic status (SES) as a determinant of health among both individuals and communities. However, relatively few studies have focused on the influence of SES and injuries. Furthermore, those that have, and the other injury studies that have included measures of SES in their analysis, have varying degrees of conceptual and methodological rigor in their use of this measure. Recent advances in data linkage and analytic techniques have, however, provided new and improved methods to assess the relationship between SES and injuries. This review summarizes the relevant literature on SES and injuries, with particular attention to study design, and the measurement and interpretation of SES. We found that increasing SES has a strong inverse association with the risk of both homicide and fatal unintentional injuries, although the results for suicide were mixed. However, the relationship between SES and nonfatal injuries was less consistent than for fatal injuries. We offer potential explanatory mechanisms for the relationship between SES and injuries and make recommendations for future research in this area.
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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 | 0.421 | 0.603 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".