Bibliographic record
Abstract
BACKGROUND: The literature demonstrates that most NICUs exceed the standard recommendations for noise levels and that high noise levels have a negative impact on patients and staff. OBJECTIVE: The objective of this research was to measure baseline noise level in an NICU, compare it to recommendations of international bodies, and evaluate the impact of a noise awareness educational program (NAEP) as a strategy to decrease it. DESIGN/METHODS: Means of hourly average noise levels in decibels (dB) were compared with the recommendations and pre- and postintervention (P = .05). RESULTS: Mean noise-level preintervention was significantly higher than recommended (58.15 vs 45 dB; P < 0.001). The participation rate in NAEP was excellent and most participants thought that the content was relevant and would change their practice. Overall, at first glance, the impact of the NAEP was not as expected: the noise levels increased nonsignificantly postintervention (58.15 vs 58.46 dB; P < .181). However, a significant increase in activity level (number of nurses and patient) was thought to be responsible for the lack of significance postintervention. After controlling for these variables, it was demonstrated that the noise level did significantly decrease postintervention (6.33 vs 5.42 dB per RN & 4.68 vs 4.08 dB per patient, P < .000). CONCLUSION: Although the efficacy of the program was significantly limited by an increase in general activity, it raised staff awareness and had important effects reflected by the significant decrease in mean noise level after standardization and the participant's comments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".