Racial disparities in job strain among American and immigrant long‐term care workers
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Nursing homes are occupational settings, with an increasing minority and immigrant workforce where several psychosocial stressors intersect. AIM: This study aimed to examine racial/ethnic differences in job strain between Black (n = 127) and White (n = 110) immigrant and American direct-care workers at nursing homes (total n = 237). METHODS: Cross-sectional study with data collected at four nursing homes in Massachusetts during 2006-2007. We contrasted Black and White workers within higher-skilled occupations such as registered nurses or licensed practical nurses (n = 82) and lower-skilled staff such as certified nursing assistants (CNAs, n = 155). RESULTS: Almost all Black workers (96%) were immigrants. After adjusting for demographic and occupational characteristics, Black employees were more likely to report job strain, compared with Whites [relative risk (RR): 2.9, 95% confidence interval (CI) 1.3 to 6.6]. Analyses stratified by occupation showed that Black CNAs were more likely to report job strain, compared with White CNAs (RR: 3.1, 95% CI: 1.0 to 9.4). Black workers were also more likely to report low control (RR: 2.1, 95% CI: 1.1 to 4.0). Additionally, Black workers earned $2.58 less per hour and worked 7.1 more hours per week on average, controlling for potential confounders. CONCLUSION: Black immigrant workers were 2.9 times more likely to report job strain than White workers, with greater differences among CNAs. These findings may reflect differential organizational or individual characteristics but also interpersonal or institutional racial/ethnic discrimination. Further research should consider the role of race/ethnicity in shaping patterns of occupational stress.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it