The Assessment of Chronic Health Conditions on Work Performance, Absence, and Total Economic Impact for Employers
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine the prevalence and estimate total costs for chronic health conditions in the U.S. workforce for the Dow Chemical Company (Dow). METHODS: Using the Stanford Presenteeism Scale, information was collected from workers at five locations on work impairment and absenteeism based on self-reported "primary" chronic health conditions. Survey data were merged with employee demographics, medical and pharmaceutical claims, smoking status, biometric health risk factors, payroll records, and job type. RESULTS: Almost 65% of respondents reported having one or more of the surveyed chronic conditions. The most common were allergies, arthritis/joint pain or stiffness, and back or neck disorders. The associated absenteeism by chronic condition ranged from 0.9 to 5.9 hours in a 4-week period, and on-the-job work impairment ranged from a 17.8% to 36.4% decrement in ability to function at work. The presence of a chronic condition was the most important determinant of the reported levels of work impairment and absence after adjusting for other factors (P < 0.000). The total cost of chronic conditions was estimated to be 10.7% of the total labor costs for Dow in the United States; 6.8% was attributable to work impairment alone. CONCLUSION: For all chronic conditions studied, the cost associated with performance based work loss or "presenteeism" greatly exceeded the combined costs of absenteeism and medical treatment combined.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".