Nanomaterials, and Occupational Health and Safety—A Literature Review About Control Banding and a Semi-Quantitative Method Proposed for Hazard Assessment.
Bibliographic record
Abstract
In recent decades, the control banding (CB) approach has been recognised as a hazard assessment methodology because of its increased importance in the occupational safety, health and hygiene (OSHH) industry.According to the American Industrial Hygiene Association, this approach originates from the pharmaceutical industry in the United Kingdom.The aim of the CB approach is to protect more than 90% (or approximately 2.7 billion) of the world's workers who do not have access to OSHH professionals and traditional quantitative risk assessment methods.In other words, CB is a qualitative or semi-quantitative tool designed to prevent occupational accidents by controlling worker exposures to potentially hazardous chemicals in the absence of comprehensive toxicological and exposure data.These criteria correspond very precisely to the development and production of engineered nanomaterials (ENMs).Considering the significant lack of scientific knowledge about work-related health risks because of ENMs, CB is, in general, appropriate for these issues.Currently, CB can be adapted to the specificities of ENMs; hundreds of nanotechnology products containing ENMs are already on the market.In this context, this qualitative or semi-quantitative approach appears to be relevant for characterising and quantifying the degree of physico-chemical and biological reactivities of ENMs, leading towards better control of human health effects and the safe handling of ENMs in workplaces.The need to greater understand the CB approach is important to further manage the risks related to handling hazardous substances, such as ENMs, without established occupational exposure limits.In recent years, this topic has garnered much interest, including discussions in many technical papers.Several CB models have been developed, and many countries have created their own nano-specific CB instruments.The aims of this research were to perform a literature review about CBs, to classify the main approaches that were developed worldwide, and then to suggest an original methodology based on the characterisation of the hazard.For this research, our team conducted a systematic literature review over the past 20 years.This approach is important in understanding the conceptual basis for CB and the model's overall effectiveness.These considerations will lead to the proposal of an original hazard assessment method based on physico-chemical and biological characteristics.Such a method should help the entire industry better understand the ability of the CB approach to limit workers' exposure, while identifying the strengths and weaknesses of the approach.Developing this practice method will help to provide relevant recommendations to workers who handle hazardous chemicals such as ENMs and to the general population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".