Is manager support related to workplace productivity for people with depression: a secondary analysis of a cross-sectional survey from 15 countries
Bibliographic record
Abstract
OBJECTIVES: To examine variations in manager reactions and support for people with depression and to investigate how these reactions are related to (1) absenteeism and (2) presenteeism due to depression among employees with self-reported depression across 15 diverse countries. DESIGN: Secondary data analysis of cross-sectional survey data. SETTING: 15 countries, diverse in geographical region and gross domestic product (GDP): Brazil, Canada, China, Denmark, France, Germany, Great Britain, Italy, Japan, Mexico, Spain, South Africa, South Korea, Turkey and the USA. PARTICIPANTS: 16 018 employees and managers (approximately 1000 per country). PRIMARY AND SECONDARY OUTCOME MEASURES: We assessed level of absenteeism as measured by number of days taken off work because of depression and presenteeism score. RESULTS: On average, living in a country with a greater prevalence of managers saying that they avoided talking to the employee about depression was associated with employees with depression taking more days off work (B 4.13, 95% CI 1.68 to 6.57). On average, living in a country with a higher GDP was marginally associated with employees with depression taking more days off of work (p=0.09). On average, living in a country with a greater prevalence of managers actively offering help to employees with depression was associated with higher levels of presenteeism (B 7.08, 95% CI 6.59 to 7.58). Higher country GDP was associated with greater presenteeism among employees with depression (B 3.09, 95% CI 2.31 to 3.88). CONCLUSIONS: Manager reactions were at least as important as country financial resources. When controlling for country GDP, working in an environment where managers felt comfortable to offer help and support to the employee rather than avoid them was independently associated with less absenteeism and more presenteeism.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".