P-143 SHIFTING THE FOCUS FROM EXPOSURE TO SOCIOECONOMIC STATUS IN OCCUPATIONAL EPIDEMIOLOGY: A PROTOCOL OF A CONCEPTUAL FRAMEWORK FOR A SOCIOECONOMIC RISK MATRIX
Bibliographic record
Abstract
Abstract Introduction Job exposure matrices (JEM) often neglect upstream socioeconomic variables and assume the exposure epidemiology lens. We propose a conceptual framework utilizing education, wealth, and healthcare access as primary indicators of occupational cardiorespiratory risk. This protocol encompasses key socioeconomic status (SES) predictors to produce a Socioeconomic Risk Matrix (SRM). Methods Prioritizing utilization in cohort studies, data will be obtained from the National Health and Nutrition Examination Survey. A random forest regression assigns each SRM variable a weighting according to its likelihood of impacting risk. Each variable contributes an ordinal risk level, where a 3-way matrix produces a 0 to 1 risk score. Relative risk of COPD will be estimated based on SRM risk quartiles using multinomial logistic regression. Results Using SRM as an interdisciplinary approach to occupational epidemiology identifies the magnitude of socioeconomic variables’ impact on occupational disease risk. We expect SRM to allow for expanded data utilization within and between occupational groups/clusters. Discussion This approach may indicate a necessity for analyzing additional SRM variables individually and inter-relatedly to support their associative impact on occupational, cardiorespiratory health outcomes. Remodelling SRM’s risk scoring structure may expose interactions between socioeconomic and lifestyle factors in the context of occupational exposure, providing a multidimensional approach to occupational risk estimation. The SRM lacks direct reproducibility, as different cohort samples reflect diverse SES combinations, however this orients future studies in occupational health to an SES-centric approach over traditional exposure-centric investigations. Conclusion SRM may act as a primer for improved SES matrices, incorporating variance in workplace and geo-cultural factors where relevant.
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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 | 0.073 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".