Description of Noise Levels in a Pharmacy Department at a University Hospital
Bibliographic record
Abstract
OBJECTIVE: Few data are available regarding noise levels in hospital pharmacies. This study mainly aimed to assess noise levels in different areas and during various activities in the pharmacy department of a tertiary care hospital affiliated with Université de Montréal in Canada and identify potential noise sources. MATERIALS AND METHODS: This cross-sectional descriptive study was conducted in the pharmacy department of Centre Hospitalier Universitaire (CHU) Sainte-Justine. A convenience sample of 30 sites was established to encompass various activities. Noise levels were measured with a sound level meter for three types of activity: office activities, storage, and drug preparation. Noise measurements were conducted for a 1-day period at each site. RESULTS: The average noise level ranged from 41.9 ± 3.4 dBA to 71.4 ± 0.4 dBA during the day and from 37.5 ± 0.4 dBA to 71.2 ± 0.1 dBA at night. The levels were 50.9 ± 5.6 dBA in offices, 58.0 ± 5.8 dBA in storage areas, and 63.9 ± 9.2 dBA in drug preparation areas. Considering noise distribution by percentile, the L10 ranged from 43.7 to 71.7 dBA, the L50 from 37.8 to 71.3 dBA, and the L90 from 37.2 to 71.1 dBA. CONCLUSION: Average noise levels varied widely within the pharmacy department of the studied hospital, and a substantial proportion of it could be due to the building's ventilation system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".