Reduction of noise in the neonatal intensive care unit using sound-activated noise meters
Bibliographic record
Abstract
OBJECTIVES: To determine if sound-activated noise meters providing direct audit and visual feedback can reduce sound levels in a level 3 neonatal intensive care unit (NICU). DESIGN/METHODS: Sound levels (in dB) were compared between a 2-month period with noise meters present but without visual signal fluctuation and a subsequent 2 months with the noise meters providing direct audit and visual feedback. RESULTS: There was a significant increase in the percentage of time the sound level in the NICU was below 50 dB across all patient care areas (9.9%, 8.9% and 7.3%). This improvement was not observed in the desk area where there are no admitted patients. There was no change in the percentage of time the NICU was below 45 or 55 dB. CONCLUSIONS: Sound-activated noise meters seem effective in reducing sound levels in patient care areas. Conversations may have moved to non-patient care areas preventing a similar change there.
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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 | 0.005 |
| 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) | 0.002 | 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".