Global patterns of workplace productivity for people with depression: absenteeism and presenteeism costs across eight diverse countries
Bibliographic record
Abstract
PURPOSE: Depression is a leading cause of disability worldwide. Research suggests that by far, the greatest contributor to the overall economic impact of depression is loss in productivity; however, there is very little research on the costs of depression outside of Western high-income countries. Thus, this study examines the impact of depression on workplace productivity across eight diverse countries. METHODS: We estimated the extent and costs of depression-related absenteeism and presenteeism in the workplace across eight countries: Brazil, Canada, China, Japan, South Korea, Mexico, South Africa, and the USA. We also examined the individual, workplace, and societal factors associated with lower productivity. RESULTS: To the best of our knowledge, this is the first study to examine the impact of depression on workplace productivity across a diverse set of countries, in terms of both culture and GDP. Mean annual per person costs for absenteeism were lowest in South Korea at $181 and highest in Japan ($2674). Mean presenteeism costs per person were highest in the USA ($5524) and Brazil ($5788). Costs associated with presenteeism tended to be 5-10 times higher than those associated with absenteeism. CONCLUSIONS: These findings suggest that the impact of depression in the workplace is considerable across all countries, both in absolute monetary terms and in relation to proportion of country GDP. Overall, depression is an issue deserving much greater attention, regardless of a country's economic development, national income or culture.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".