Pre-Post Pilot Study of Noise Levels at a University Hospital Center Pharmacy Department
Bibliographic record
Abstract
CONTEXT: Noise interferes with attention, speech understanding, detection of other auditory stimuli, and leads to interruptions. OBJECTIVES: This is a pre-post cross-sectional observational study aimed at measuring noise levels in a 500-bed mother-child university hospital center pharmacy department after the implementation of corrective measures, such as work zone reorganization, digitization of the prescription process, and education about the impact of some work habits. RESULTS: A total of 24 measurement points (70 noise level measurements) were taken in 2007 (pre) compared to 30 measurement points (59 noise level measurements) in 2012 (post). There was a statistically significant difference in the average values of the day and night noise measurements (day 59.4 ± 5.3 dB(A) vs night 52.3 ± 8.0 dB(A); P < .001). There were no statistically significant differences in the average values of the noise measurements taken during daytime between the prestudy and poststudy phases (pre 59.4 ± 5.3 vs post 58.07 ± 6.01 dB(A); P = .22). CONCLUSION: Few data exist on the noise levels in hospital pharmacy departments. In spite of the corrective measures implemented, reducing noise levels in pharmacy departments was difficult to achieve. Average values of approximately 60 dB(A) seem to be unacceptable for work that requires a high level of 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".