Racial disparities in job strain among American and immigrant long‐term care workers
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".