Beyond Nine To Five: Is Working To Excess Bad For Health?
Bibliographic record
Abstract
This study investigated whether two sides of working to excess, namely working long hours and a compulsive work mentality (workaholism), are detrimental for employee health by using biomarkers of metabolic syndrome, a direct precursor of cardiovascular diseases. In addition, we examined if working to excess has the same health outcomes for employees who enjoy their work versus employees who do not. Despite the common sense belief that working long hours is bad for health, we did not find a relationship between work hours and risk factors of metabolic syndrome (RMS; e.g. high blood pressure, elevated cholesterol levels) in a study among 763 employees. Instead, we found that workaholism was positively related to RMS, but only when work engagement was low. Surprisingly, we found that workaholism was negatively related to RMS in the highly engaged group. When further exploring mediation mechanisms, we found that workaholism, but not work hours, was related to reduced subjective well-being (e.g. depressive feelings, sleep problems), which in turn elicited a physical health impairment process. We also found that, compared with nonengaged workaholics, engaged workaholics had more resources, which they may use to halt the health impairment process. Our findings underscore that not long hours per se, but rather a compulsive work mentality is associated with severe health risks, and only for employees who are not engaged at work. Work engagement may actually protect workaholics from severe health risks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".