Accident experiences and reporting practices in Canadian chemistry and biochemistry labs: A pilot investigation
Bibliographic record
Abstract
Accidents in chemistry and biochemistry laboratories are a regular occurrence and have been associated with injuries, property damage, and deaths. However, despite a high prevalence rate of accident involvement reported in previous investigations of academic lab personnel (approximately 30%), little is known about the context in which academic lab accidents occur. Previous findings also suggest a high degree of accident underreporting (25–40%), but again, little is known about this phenomenon. Pilot data was gathered from a convenience sample of 104 students and postdoctoral fellows in chemistry-related fields through an online survey. Results showed a high level of accident involvement (56.7%); of that number, most of those (65.9%) had been involved in multiple accidents. Most accidents involved only personal injuries and happened on a weekday afternoon with other lab members present. The majority of participants reported wearing multiple types of PPE at the time; however, adherence rates for any one type of equipment (e.g., goggles, gloves, coat) was less than 50%. Most (69.6%) reported their accidents to multiple individuals and were at least somewhat or very satisfied (81.2%) with their decision to report. Participants who chose not to report their accidents reported barriers such as beliefs that the accident was not severe, concerns about judgment, self-blame, and not knowing they had to report the accident or how. Implications for safety training and reporting practices are considered.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".