The Healthy LifeWorks Project
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between health risks and absenteeism and drug costs vis-a-vis comprehensive workplace wellness. METHODS: Eleven health risks, and change in drug claims, short-term and general illness calculated across four risk change groups. Wellness score examined using Wilcoxon test and regression model for cost change. RESULTS: The results showed 31% at risk; 9 of 11 risks associated with higher drug costs. Employees moving from low to high risk showed highest relative increase (81%) in drug costs; moving from high to low had lowest (24%). Low-high had highest increase in absenteeism costs (160%). With each risk increase, absenteeism costs increased by $CDN248 per year (P < 0.05) with average decrease of 0.07 risk factors and savings $CDN6979 per year. CONCLUSIONS: Both high-risk reduction and low-risk maintenance are important to contain drug costs. Only low-risk maintenance also avoids absenteeism costs associated with high 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.014 |
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".