Prevalence of Overnight Work (1 a.m. to 5 a.m.) Among United States Workers
Bibliographic record
Abstract
BACKGROUND: Many factors have resulted in the normalization of nonstandard work schedules in recent decades, including globalization requiring working across time zones and growing demands for goods and services. This paper provides national estimates of overnight work in the USA. METHODS: We used cross-sectional data from the 2015 National Health Interview Survey (n = 19,386 US employed adults ≥ 18 years). This survey contained a unique definition of overnight work (i.e., between 1:00 a.m. and 5:00 a.m.), based on the window of circadian low. Weighted prevalence rates were provided across categories of sociodemographic characteristics, health status, health behaviors, and occupational factors. RESULTS: We estimated more than 21 million US employed adults experienced overnight work (14.2%). Higher prevalence was found among men (17.8%), non-Hispanic Black adults (17.2%), non-US born adults (11.2%), those with some college (15.9%) or a high school (16.7%) education, or living in the Midwest region (15.8%). Compared to those sleeping 7-9 h (10.5%), higher percentages of adults working overnight slept < 7 h (21.4%) and > 9 h (17.0%). Increasing prevalence was observed with increasing weekly work hours (p < 0.0001). Higher prevalence was reported among multiple job holders (19.5%). Industries and occupations with the greatest percentage of overnight workers were Transportation, Warehousing and Utilities (29.3%), and Protective Services (47.4%). CONCLUSION: Our estimates of overnight work in 2015 are almost five times higher than estimates from 2004. Given that overnight work has been associated with adverse safety and health outcomes, additional policies and programs are needed to protect this growing population of workers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".