Accident Experiences and Reporting Practices in Canadian\nChemistry and Biochemistry Laboratories: A Pilot Investigation
Bibliographic record
Abstract
Accidents in chemistry and biochemistry\nlaboratories are a regular\noccurrence and have been associated with injuries, property damage,\nand deaths. However, despite a high prevalence rate of accident involvement\nreported in previous investigations of academic lab personnel (approximately\n30%), little is known about the context in which academic lab accidents\noccur. Previous findings also suggest a high degree of accident underreporting\n(25–40%), but again, little is known about this phenomenon.\nPilot data was gathered from a convenience sample of 104 students\nand postdoctoral fellows in chemistry-related fields through an online\nsurvey. Results showed a high level of accident involvement (56.7%);\nof that number, most of those (65.9%) had been involved in multiple\naccidents. Most accidents involved only personal injuries and happened\non a weekday afternoon with other lab members present. The majority\nof participants reported wearing multiple types of PPE at the time;\nhowever, adherence rates for any one type of equipment (e.g., goggles,\ngloves, coat) was less than 50%. Most (69.6%) reported their accidents\nto multiple individuals and were at least somewhat or very satisfied\n(81.2%) with their decision to report. Participants who chose not\nto report their accidents reported barriers such as beliefs that the\naccident was not severe, concerns about judgment, self-blame, and\nnot knowing they had to report the accident or how. Implications for\nsafety training and reporting practices are considered.
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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 | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".