Sitting for long periods is associated with impaired work performance during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: The unprecedented coronavirus disease 2019 (COVID-19) pandemic and the corresponding government state of emergency have dramatically changed our workstyle, particularly through implementing teleworking and social distancing. We investigated the degree to which people's work performance is affected and the association between sedentary behavior under the state of emergency and worsened work performance during the COVID-19 pandemic, as previous studies have suggested that sedentary behavior decreases work performance. METHODS: We used data from the Japan "COVID-19 and Society" Internet Survey (JACSIS) study, a cross-sectional, web-based, self-reported questionnaire survey. The main outcome was change in work performance after the COVID-19 pandemic compared with that before the pandemic. We analyzed the association between the change in work performance and sitting duration under the state of emergency, adjusted for work-related stress, participants' demographics, socio-economic status, health-related characteristics, and personality. RESULTS: The change of work environment from the pandemic decreased work performance in 15% of workers, which was 3.6 times greater than the number of workers reporting increased performance in 14 648 workers (6134 women and 8514 men). Although telework both improved and worsened performance (odds ratio [OR], 95% confidence interval [CI] = 2.0, 1.6-2.5 and 1.7, 1.5-1.9, respectively), sitting for long periods after the state of emergency was significantly associated only with worsened performance (OR, 95% CI = 1.8, 1.5-2.2) in a dose-response manner. CONCLUSION: Sitting duration is likely a risk barometer of worsened work performance under uncertain working situations, such as the COVID-19 pandemic.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".