Mathematical Procedure to Adjust for the Healthy Worker Effect: The Case of Firefighting, Diabetes, and Heart Disease
Bibliographic record
Abstract
This article presents a mathematical procedure to adjust for one component of the healthy worker effect (HWE), namely, the healthy hired effect, on diabetes in the case of firefighting and heart disease. Three examples from real studies are given to illustrate, step-by-step, the application of the mathematical procedure. The mathematical procedure can be applied to adjust for other components of the HWE (e.g., the low-risk hired effect on obese individuals and smokers). In such cases, additional information will be needed to use the mathematical procedure. Results of applying the mathematical procedure in the case of firefighting and heart disease revealed the rather unexpected results that adjusting for diabetes selection on hiring leads to only a 3% to 9% increase in the magnitude of ratio statistics such as the standardized mortality ratio. It might be argued that the HWE from one component such as the healthy hired effect on diabetes might be small, but together with other components, the HWE might be large. Further investigation will be needed to support this argument.
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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 | 0.051 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".