Safety Data Sheets as a Hazard Communication Tool: An Assessment of Suitability and Readability
Bibliographic record
Abstract
Background: Safety data sheets (SDSs) are hazard communication materials that accompany chemicals/hazardous products in the workplace. Many SDSs contain dense, technical text, which places considerable comprehension demands on workers, especially those with lower literacy skills. The goal of this study was to assess SDSs for readability, comprehensibility, and suitability (i.e., fit to the target audience). Methods: The Suitability Assessment of Materials (SAM) tool assessed SDSs for suitability and readability. We then amended the SAM tool to further assess SDSs for comprehensibility factors. Both the original and amended SAM tool were used to score 45 randomly selected SDSs for content, literacy demand, graphics, and layout/typography. Results: SDSs performed poorly in terms of readability, suitability, and comprehensibility. The mean readability scores were Flesch-Kincaid Grade Level (9.6), Gunning Fog index (11.0), Coleman-Liau index (13.7), and Simple Measure of Gobbledygook index (10.7), all above the recommended reading level. The original SAM graded SDSs as "not suitable" for suitability and readability. When the amended SAM was used, the mean total SAM score increased, but the SDSs were still considered "not suitable" when adding comprehensibility considerations. The amended SAM tool better identified content-related issues specific to SDSs that make it difficult for a reader to understand the material. Conclusions: In terms of readability, comprehensibility, and suitability, SDSs perform poorly in their primary role as a hazard communication tool, therefore, putting workers at risk. The amended SAM tool could be used when writing SDSs to ensure that the information is more easily understandable for all audiences.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".