Safety culture and safety compliance in academic laboratories: A Canadian perspective
Bibliographic record
Abstract
Laboratories are inherently dangerous work environments. In fact, there have been a number of incidents, including fatalities, reported from academic laboratories situated in the United States and worldwide. In many instances, a lack of safety compliance and poor safety culture have been attributed to causing these laboratory accidents. Oddly, no major incident has apparently been reported from an academic lab based in Canada. Therefore, the goal of this pilot study was to examine the safety culture and level of safety compliance that exists within a medium-sized Canadian university. This was conducted by administering an online survey to participants in which a number of the questions were adapted from a 2012 international safety culture study. The results indicated a potential gap between how safety is perceived and managed at the participating institution. For instance, while 90% of the participants indicated that safety is important to them, 9% revealed they lacked safety training, 27% said they do not conduct any form of risk assessment before lab work, and only 40% said they always wear their personal protective equipment when performing lab work. In addition, although 88% indicated that safety is a high priority in their lab, 39% felt that the safety in their lab could be improved. These results suggest that the labs at this Canadian institution not only has issues with safety compliance but also lacks a strong and positive safety culture. Overall, these findings lend further evidence that poor safety compliance and safety culture in university labs is a global phenomenon and warrants urgent attention.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".