P-372 Safety data sheets as a hazard communication tool: an assessment of suitability and readability
Bibliographic record
Abstract
<h3>Introduction</h3> Safety data sheets (SDSs) are printed materials designed to communicate the hazards associated with using chemicals/hazardous products in the workplace. SDSs are highly technical in nature, often containing dense, ambiguous text, which places considerable comprehension demands on workers, especially those with lower literacy skills. <h3>Objective</h3> To assess the suitability and readability of SDSs as a hazard communication tool for workers. <h3>Methods</h3> A random sample of 50 SDSs compliant with WHMIS 2015 were extracted from the CCOHS m(SDS) database. The Suitability of Materials (SAM) tool, originally designed to evaluate patient education materials, was used to assess SDSs for content, literacy demand, use of graphics, and layout/typography. To account for legislated content requirements under WHMIS 2015, an amended SAM tool was also developed for scoring. Readability software was used to determine the reading grade level required to understand SDSs based on several common formulas/indices. <h3>Results</h3> When the original SAM tool was used, the mean total SAM score was ‘not suitable’ (30.22%). When the amended SAM tool was used, the mean total SAM score increased to ‘adequate’ (64.43%). The mean readability scores were as follows: Flesch-Kincaid Grade Level (10.2), Gunning-Fox Index (8.5), Coleman-Liau Index (12.2), and Simple Measure of Gobbledygook index (10.2). <h3>Conclusion</h3> Even though the amended SAM tool was better able to identify content-related issues specific to SDSs, the use of SDSs as a hazard communication tool needs improvement. The SDSs analyzed required a reading grade level between the 8th and 12th grades. These levels exceed the 6th-grade reading level recommended to ensure that 75% of adult Americans can read the material without difficulty. Overall, chemical/hazardous product manufacturers should use readability assessments together with the amended SAM tool when writing SDSs to ensure that the written information is easily understandable for all audiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".