Can operating room nurses accurately classify noise exposures?
Bibliographic record
Abstract
Worker’s qualitative chemical exposure estimates when compared to estimates made by experts or with monitoring devices have been found to be accurate. But almost no research exists on worker’s ability to classify noise exposure. This investigation took place within a multi-hospital intervention study in which OR nurses completed a questionnaire at the end of each surgery and answered, ‘‘During this surgery could you easily hear: quiet talking, normal talking, or loud talking?’’ In 255 surgeries, noise was measured using a sound level meter, for a minimum of 15 min, while in 68 surgeries noise was measured for >70% of the surgery. In the 255 surgeries in which noise was measured for 15 min or >, it was found that nurses who heard quiet talking were exposed to 62.8 dB(A) and that nurses who heard normal or loud talking were exposed to 65.1 dB(A), a difference that was statistically significant (p=0.019). In the 68 surgeries where noise measurements lasted more than 70% of the surgery, nurses who heard quiet talking were exposed to 64.0 dB(A), while nurses who heard normal or loud talking were exposed to 67.3 dB(A), a difference that was borderline significant (p=0.07). Nurses can distinguish between noise that interferes with quiet talking during surgery and noise that does not. [Work funded by Ontarios Workplace Safety Insurance Board.]
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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 | 0.011 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".