Striving for Optimum Noise-Decreasing Strategies in Critical Care
Bibliographic record
Abstract
To identify baseline sound levels, patterns of sound levels, and potential barriers and facilitators to sound level reduction. The study setting was neonatal and pediatric intensive care units in a tertiary care hospital. Participants were staff in both units and parents of currently hospitalized children or infants. One 24-hour sound measurements and one 4-hour sound measurement linked to observed sound events were conducted in each area of the center's neonatal intensive care unit. Two of each measurement type were conducted in the pediatric intensive care unit. Focus groups were conducted with parents and staff. Transcripts were analyzed with descriptive content analysis and themes were compared against results from quantitative measurements. Sound levels exceeded recommended standards at nearly every time point. The most common code was related to talking. Themes from focus groups included the critical care context and sound levels, effects of sound levels, and reducing sound levels-the way forward. Results are consistent with work conducted in other critical care environments. Staff and families realize that high sound levels can be a problem, but feel that the culture and context are not supportive of a quiet care space. High levels of ambient sound suggest that the largest changes in sound levels are likely to come from design and equipment purchase decisions. L10 and Lmax appear to be the best outcomes for measurement of behavioral interventions.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".