Results of the 2019 Survey of Engineered Nanomaterial Occupational Health and Safety Practices
Bibliographic record
Abstract
In collaboration with RTI International, the U.S. National Institute for Occupational Safety and Health (NIOSH) administered a survey to North American companies working with nanomaterials to assess health and safety practices. The results would contribute to understanding the impact of the efforts made by the NIOSH Nanotechnology Research Center (NTRC) in communicating occupational health and safety (OHS) considerations for workers when handling these materials. The survey, developed by RAND Corporation, was conducted online from September 2019-December 2019. Forty-five companies or organizations in the U.S. and Canada that fabricate, manufacture, handle, dispose, or otherwise use nanomaterials completed the survey. The survey was designed to answer research questions regarding the nanomaterials in use, which resources the companies have consulted for OHS guidance, and the overall OHS culture at the companies. Other questions specifically addressed whether the companies interacted with NIOSH or NIOSH resources to inform OHS policies and practices. Among participating companies, 57.8% had a maximum of 50 employees. Gold nanoparticles and polymers were most common (n = 20; 45.5% each), followed by graphene (36.4%), carbon nanotubes and nanofibers (34.1%), and zinc oxide nanoparticles (31.8%). Environmental monitoring was performed by 31.8% of the companies. While 88.9% of the companies had laminar flow cabinets, only 67.5% required it to be used with ENMs. Information and training programs were indicated by 90% of the sample, and only 29.6% performed specific health surveillance for ENM workers. Personal protective equipment primarily included gloves (100%) and eye/face protection (97.7%). More than a third (37.8%) of the respondents reported using at least one NIOSH resource to acquire information about safe handling of ENMs. The small number of companies that responded to and completed the survey is a considerable limitation to this study. However, the survey data are valuable for gauging the reach and influence of the NIOSH NTRC on nano OHS and for informing future outreach, particularly to small businesses.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".