Measuring the effects of acoustical environments on nurses in health-care facilities: A pilot study.
Bibliographic record
Abstract
This paper summarizes the methods and results of a pilot ecological study conducted in four health-care facilities (acute-care, community-care, and long-term-care). The objective was to consolidate and test tools for exposure assessment and the investigation of study outcomes, in particular, stress. Area and personal monitoring was performed. Nurse noise exposures were monitored. Full-shift monitoring of sound levels was performed, and conventional acoustical parameters derived; new acoustical descriptors including occurrence rate and peakiness were also determined. Two questionnaire scales were developed: a study questionnaire to assess perception of the acoustical environment and of work- and noise-related stresses, and a daily diary to capture variations in the perceived stress and document aggressive events. The study questionnaire was found to measure disturbance, impaired communication, and mental fatigue. Biological markers of noise-related stress (salivary cortisol and heart-rate variability) were collected. Exposure measures were correlated with outcomes; while the results were often not statistically significant due to small sample sizes, they identified interesting relationships and validated the measurement tools for future use. Long-term-care was identified as the most acoustically-critical environment both from a physical-acoustical perspective and from the perspective of workers.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".