The Assessment of Chronic Health Conditions on Work Performance, Absence, and Total Economic Impact for Employers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it