Toward a comprehensive hospital noise reduction research program
Bibliographic record
Abstract
Over the last few years there has been a growing interest in the control of hospital noise. This is prompted by a number of drivers: the recognition that hospital noise is a top complaint of patients, staff, and visitors; the move to a digital hospital and the impediment of speech recognition in very noisy environments; the implications of HIPAA and speech privacy on hospitals; and concerns about safety when communication is compromised by noise. Although there are now a number of studies of various issues associated with hospital noise, each views only a small piece of the very complicated problem. This presentation outlines a comprehensive noise research program designed to link acoustical measures with noise interventions and with staff and patient outcomes. Only through such an approach will it be possible to answer important questions such as which noise interventions are most effective and what hospital design strategies are most effective in terms of noise mitigation.
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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 | 0.124 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".