A cross-sectional analysis of work schedule notice and depressive symptoms in the United States
Bibliographic record
Abstract
The implementation of last-minute work scheduling practices, including fluctuations in work hours, shift cancellations, and short notice, reflects a new norm in employment in the United States. This study aimed to investigate whether work schedule notice of ≤2 weeks was associated with high depressive symptoms. We used data from the 2019 cycle of the National Longitudinal Survey of Youth 1997 (N = 4963 adults aged 37-42 years). Using adjusted gender-stratified modified Poisson models, we tested the association between schedule notice (≤2 weeks, >2 weeks, consistent scheduling) and high depressive symptoms. Presence of high depressive symptoms was assessed using the 7-item Center for Epidemiologic Studies Depression (CES-D) Short-Form scale and defined as CES-D-SF ≥8. Respondents reporting >2 weeks schedule notice (versus ≤2 weeks) were disproportionately non-Hispanic Black or Hispanic and resided in the South and/or in a rural area. High depressive symptoms were 39% more prevalent among women with schedule notice of ≤2 weeks compared to those with >2 weeks notice (Prevalence Ratio [PR]: 1.39, 95% Confidence Interval (CI): 1.07, 1.80). We did not observe an association among men (PR: 1.06, 95% CI: 0.75, 1.50). Schedule notice of ≤2 weeks was associated with a greater burden of high depressive symptoms among US women. Policies to reduce precarious work scheduling practices should be further evaluated for their impacts on mental health.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".