Development of a Web-Based Tool for Risk Assessment and Exposure Control Planning of Silica-Producing Tasks in the Construction Sector
Bibliographic record
Abstract
We describe the development and implementation of a novel, on-line risk assessment tool for respirable crystalline silica (RCS) exposure for use in the construction sector. It was motivated by the introduction of new OHS regulation in British Columbia that allowed for the substitution of exposure measurement data with "objective air monitoring data" collected at "equivalent work operations." This allowance encouraged the introduction of quantitative risk assessment in a notoriously challenging work environment but it was concluded that without assistance, the typical construction employer would struggle to identify, extract, and interpret validate objective data. The tool described here was based on a continually-updatable RCS exposure database, and a predictive regression model based on the database to generate exposure risk estimates. The model was embedded in an adaptive web-based application that can be run on common platforms. The design followed standard web conventions and features so that no specialized training is required for its use. It was designed to be usable by end-users with varying expertise, including non-OHS experts. Users describe the RCS-dust generating task they will perform, and associated control measures. The tool estimates both uncontrolled and controlled task-based exposure concentrations. Using additional information entered by the user, the on-line tool generates an "exposure control plan" or ECP, a legally regulated document for those undertaking work potentially exposing workers to RCS particulate. The development of the tool was a community-based, tri-partite effort of the local OHS regulator, construction employers, and exposure scientists. In addition to being a practical risk assessment tool, the designers wanted it to function as an educational tool, and that it should explore novel methods for exposure data collection and use. The strengths of this approach include the publicly shared updateable database that encourages continuous improvement and illustrates best practices; and the timely and cost effective collection and sharing of exposure data in a value-added manner. It is however limited to a single task per ECP, and only considers exposure to task operators, and not adjacent workers. Currently in BC, users generate up to 3,900 ECP's per year with the tool.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".