Physicians' Perceptions of Background Noise in a Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to measure noise levels in a tertiary care pediatric emergency department (ED) and to identify attending staff physicians' and first-year residents' perceptions of background noise levels and its impact on communication and teaching. METHODS: A mixed methodology was used in this study. A sound level meter measuring 30 to 140 dB was placed in the ED for a week. All consenting staff physicians and first-year residents were surveyed using a semistructured questionnaire during the study period to assess their perceptions of background noise and its impact. Descriptive statistics were used for quantitative analysis. Narrative answers were coded and analyzed using the method of meaning condensation to assess the impact of background noise on both communication and teaching. RESULTS: The average noise level in the ED is 68.73 dB for a 24-hour period. The number sound peaks higher than 80 dB, with an average of 309 dB/d (minimum, 193 dB; maximum, 461 dB). Only 35% of staff physicians' surveys and 22% of residents' surveys identified the noise levels to be uncomfortable. However, background noise in the ED was perceived as stressful, affecting interaction, communication, and teaching between residents and staff physicians. Staff physicians and residents stated that they feel helpless when it is too noisy and did not have good strategies to reduce background noise in the ED. CONCLUSIONS: The high background noise levels in a pediatric ED are perceived as stressful and interfering with communication and teaching. Noise levels in EDs should be measured, and noise reduction strategies should be implemented because physicians are not consistent in identifying excessive noise levels.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".