Evaluating the Burden of Job Stress From the Public-Health and Economic Viewpoints:
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
The evaluation of the burden of job stress on both the number of cases of diseases (morbidity and mortality) and the resulting economic costs are key questions in public health. However, work in this area remains only very sparse. We here underline the importance of such a calculation, and briefly present a feasible estimation method (that of attributable fractions) and its limitations. This method uses epidemiological data on the relative risk of disease associated with a given job stress risk factor, and the prevalence of exposure to this factor. The associated difficulties revolve around the need for robust and consistent epidemiological data from prospective large-sample etiological studies. The resulting estimates of the evaluation of the burden of job stress provide useful information for decision-making regarding the allocation of resources for prevention purposes.
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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 | 0.004 | 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) | 0.002 | 0.001 |
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