Accuracy, comprehensibility, and use of material safety data sheets: A review
Bibliographic record
Abstract
OBJECTIVES: Material safety data sheets (MSDSs) are used in workplaces to communicate to workers the hazards of chemical products. This article describes a review of the peer-reviewed scientific literature regarding the accuracy, comprehensibility and use of MSDSs in the workplace. METHODS: Articles were retrieved via a systematic search of indexes and databases, followed by hand searching and citation index searching. Two reviewers independently read and coded the articles using an iterative matrix. RESULTS: Of the 280 unique articles retrieved, 24 fit the review criteria. Eligible articles included a range of methodologies: laboratory analyses, site audits, surveys and qualitative inquiry. Articles were grouped into three main topic categories: accuracy and completeness, awareness and use, and comprehensibility. Accuracy and completeness were found to be relatively poor, with the majority of studies presenting evidence that the MSDSs under review did not contain information on all the chemicals present, including those known to be serious sensitizers or carcinogens. Poor presentation and complex language were consistently associated with low comprehensibility among workers. Awareness and use of MSDSs was suboptimal in workplaces where these factors were studied. CONCLUSIONS: Despite the fact that these studies varied in methodology and spanned a period of more than 15 years, a number of common themes emerged regarding inaccuracies, incompleteness, incomprehensibility and overall low use of MSDSs. The results of the literature review suggest that there are serious problems with the use of MSDSs as hazard communication tools. The article concludes with recommendations for governments, regulatory bodies, and occupational health and safety personnel to seriously reassess the ways in which MSDSs are written, monitored, regulated, and used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".