Sickness absence with common mental disorders and antidepressant prescriptions across different employment branches during as compared to before the Covid-19 pandemic—an observational study covering the Swedish population aged 18–65 years
Bibliographic record
Abstract
Few studies have examined the implications of the Covid-19 pandemic on mental health across different employment branches. This study investigated the impact of the pandemic on long-term sickness absence (SA) with common mental disorders (CMDs) and antidepressant prescriptions in different employment branches and age groups in Sweden. Using national registers, we observed the Swedish population (18-65 years) with gainful employment quarterly from 2018 to 2021. An interrupted time-series design was employed to examine changes in trends of incidence rates (IRs) for (i) long-term (>90 days) SA with CMDs and for (ii) antidepressant prescriptions across eight employment branches during versus pre-pandemic. Analyses were stratified by age group. There was no evidence of outcome changes in the entire working age population. However, compared to pre-pandemic levels, the IRs of long-term SA with CMD increased by 5.9% per quarter for those working in the cultural sector [95% confidence interval (CI): 2.2%-9.8%], 3.4% in trade and transportation (95% CI: 0.4%-6.4%), and 5.5% in manufacturing and services (95% CI: 1.5%-9.7%) as well as among individuals aged 56-64. Incident antidepressant prescription rates were marginally higher for workers in construction (1.1% annual increase; 95% CI: 0.1%-2.1%), culture (1.4%; 0.7%-2.0%), and trade and transportation (0.9%; 0.1%-1.7%). While the risk of CMD-related long-term SA or incident antidepressant prescription in Swedish workers did not appear to be impacted by the pandemic, certain employment branches and older individuals were negatively affected in terms of both outcomes. Targeted countermeasures and initiatives to improve well-being are necessary for vulnerable groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".