O24-1 Characterisation of the selective recording of sample results in osha’s imis databank
Bibliographic record
Abstract
Objectives The Integrated Management Information System (IMIS) is the largest multi-industry source of exposure measurements available in North America. In 2010, the Occupational Safety and Health Administration (OSHA) released a second databank, the Chemical Exposure Health Data (CEHD), which contains analytical results of samples collected by OSHA inspectors. However, the two databanks only partially overlap. We investigated the selective recording of sample results into IMIS from CEHD. Methods This analysis was based on personal exposure measurements of 78 agents from 1984–2009. The association between 9 variables (level of exposure coded as detected vs. non-detected (ND), panel status, sampling time, issuance of a citation, presence of other detected levels in inspection, year, OSHA region, amount of penalty, and establishment size) and a CEHD sample record being present in IMIS was analysed using modified Poisson regression. Results A total of 588 818 CEHD measurements were examined. The overall proportion of CEHD measurements recorded into IMIS was 38% (50% for detected and 29% for ND measurements). Higher probability of recording of detected vs. ND measurement depended on whether it was part of a panel (risk ratio (RR) = 1.70, 95% confidence interval (CI): 1.67–1.73) or single determination of an agent (RR = 1.24, 95% CI: 1.21–1.26). Probability of recording increased from 1984 to 2009 for measurements of a single agent, but remained constant for samples measured on panels. Some OSHA regions had probability of recording two times higher than others. None of the other variables were associated with a CEHD sample record being present in IMIS. Conclusions Our results indicate that the under-reporting of measurements in IMIS is differential: ND samples (especially the panel ND samples) seem less likely to be recorded in IMIS than other samples. The degree of bias that this selective recording indicates remains to be understood.
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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 | 0.031 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".