Effect of work schedule on prospective antidepressant prescriptions in Sweden: a 2-year sex-stratified analysis using national drug registry data
Bibliographic record
Abstract
INTRODUCTION: Depression-related mood disorders affect millions of people worldwide and contribute to substantial morbidity and disability, yet little is known about the effects of work scheduling on depression. This study used a large Swedish survey to prospectively examine the effects of work schedule on registry-based antidepressant prescriptions in females and males over a 2-year period. METHODS: The study was based on an approximately representative sample (n=3980 males, 4663 females) of gainfully employed participants in the Swedish Longitudinal Occupational Survey of Health. Sex-stratified analyses were conducted using logistic regression. For exposure, eight categories described work schedule in 2008: 'regular days' (three categories of night work history: none, ≤3 years, 4+ years), 'night shift work', 'regular shift work (no nights)', 'rostered work (no nights)', 'flexible/non-regulated hours' and 'other'. For the primary outcome measure, all prescriptions coded N06A according to the Anatomical Therapeutic Chemical System were obtained from the Swedish National Prescribed Drug Register and dichotomised into 'any' or 'no' prescriptions between 2008 and 2010. Estimates were adjusted for potential sociodemographic, health and work confounders, and for prior depressive symptoms. RESULTS: In 2008, 22% of females versus 19% of males worked outside of regular daytime schedule. Registered antidepressant prescription rates in the postsurvey period were 11.4% for females versus 5.8% for males. In fully adjusted models, females in 'flexible/non-regulated' schedules showed an increased OR for prospective antidepressant prescriptions (OR=2.01, 95% CI=1.08 to 3.76). In males, odds ratios were most increased in those working 'other' schedules (OR=1.72, 95% CI=0.75 to 3.94) and 'Regular days with four or more years' history of night work' (OR=1.54, 95% CI=0.93 to 2.56). CONCLUSIONS: This study's findings support a relationship between work schedule and prospective antidepressant prescriptions in the Swedish workforce. Future research should continue to assess sex-stratified relationships, using detailed shift work exposure categories and objective registry data where possible.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".