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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".