Work or Place? Assessing the Concurrent Effects of Workplace Exploitation and Area-of-Residence Economic Inequality on Individual Health
Bibliographic record
Abstract
Building on previous multilevel studies in social epidemiology, this cross-sectional study examines, simultaneously, the contextual effects of workplace exploitation and area-of-residence economic inequality on social inequalities in health among low-income nursing assistants. A total of 868 nursing assistants recruited from 55 nursing homes in Kentucky, Ohio, and West Virginia were surveyed between 1999 and 2001. Using a cross-classified multilevel design, the authors tested the effects of area-of-residence (income inequality and racial segregation), workplace (type of nursing home ownership and managerial pressure), and individual-level (age, gender, race/ethnicity, health insurance, length of employment, social support, type of nursing unit, preexisting psychopathology, physical health, education, and income) variables on health (self-reported health and activity limitations) and behavioral outcomes (alcohol use and caffeine consumption). Findings reveal that overall health was associated with both workplace exploitation and area-of-residence income inequality; area of residence was associated with activity limitations and binge drinking; and workplace exploitation was associated with caffeine consumption. This study explicitly accounts for the multiple contextual structure and effects of economic inequality on health. More work is necessary to replicate the current findings and establish robust conclusions on workplace and area of residence that might help inform interventions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".