Measurement Error and Model Specification in Determining How Duration of Tasks Affects Level of Occupational Exposure
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
Statistical modeling of determinants of exposure ascertained in large-scale surveys is an increasingly popular approach to both (i) identifying effective occupational exposure controls that arise in 'natural experiments' and (ii) predicting how altering some working conditions may impact exposure levels. This paper sheds light on two underappreciated methodological challenges of such studies. First, I examine the impact of measurement error in the observed determinant of exposure on an investigator's ability to correctly rank the determinants of exposure in terms of their exposure rate (one aspect of how important a give determinant is). Simultaneously, I consider the issue of whether empirical models fitted for the sake of statistical convenience actually reflect the physical reality that is being modeled and how this may affect the answer to the question about ranking determinants of exposure. These general issues are examined in the context of the 'time per task' determinant of exposure and true exposure model that states that exposure is equal to product of exposure rate and duration of a task. Simulation studies were conducted and their conclusions applied in re-examining the data on the impact of duration of some key task on exposure levels to flour dust among bakers. The simulation study demonstrated that bias due to measurement error in observed effects can be either positive or negative. The main conclusion is that the correct ranking of exposure rates can be obtained from both true and poorly specified exposure models, but can be severely distorted by errors in estimates of the duration of tasks performed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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