Trends in OSHA Compliance Monitoring Data 1979–2011: Statistical Modeling of Ancillary Information across 77 Chemicals
Bibliographic record
Abstract
OBJECTIVES: The Integrated Management Information System (IMIS) is the largest multi-industry source of exposure measurements available in North America. However, many have suspected that the criteria through which worksites are selected for inspection are related to exposure levels. We investigated associations between exposure levels and ancillary variables in IMIS in order to understand the predictors of high exposure within an enforcement context. METHODS: We analyzed the association between nine variables (reason for inspection, establishment size, total amount of penalty, Occupational Safety and Health Administration (OSHA) plan, OSHA region, union status, inspection scope, year, and industry) and exposure levels in IMIS using multimodel inference for 77 agents. For each agent, we used two different types of models: (i) logistic models were used for the odds ratio (OR) of exposure being above the threshold limit value (TLV) and (ii) linear models were used for exposure concentrations restricted to detected results to estimate percent increase in exposure level, i.e. relative index of exposure (RIE). Meta-analytic methods were used to combine results for each variable across agents. RESULTS: A total of 511,047 exposure measurements were modeled for logistic models and 299,791 for linear models. Higher exposures were measured during follow-up inspections than planned inspections [meta-OR = 1.61, 95% confidence interval (CI): 1.44-1.81; meta-RIE = 1.06, 95% CI: 1.03-1.09]. Lower exposures were observed for measurements collected under state OSHA plans compared to measurements collected under federal OSHA (meta-OR = 0.82, 95% CI: 0.73-0.92; meta-RIE = 0.86, 95% CI: 0.81-0.91). A 'high' total historical amount of penalty relative to none was associated with higher exposures (meta-OR = 1.54, 95% CI: 1.40-1.71; meta-RIE = 1.18, 95% CI: 1.13-1.23). CONCLUSIONS: The relationships observed between exposure levels and ancillary variables across a vast majority of agents suggest that certain elements of OSHA's process of selecting worksites for inspection influence the exposure levels that OSHA inspectors encounter. Nonetheless, given the paucity of other sources of exposure data and the lack of a more demonstrably representative data source, our study considers the use of IMIS data for the estimation of exposures in the broader universe of worksites in the USA.
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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 | 0.063 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.026 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".