Room for improvement: noise on a maternity ward
Bibliographic record
Abstract
BACKGROUND: For mothers who have just given birth, the postpartum hospital stay is meant to promote an environment where resting, healing and bonding can take place. New mothers, however, face many interruptions throughout the day including multiple visitors and noise caused by medical equipment, corridor conversations and intercom announcements. This paper argues that disruptions and noise on a maternity ward are detrimental to the healing process for new mothers and their newborns and healthcare decision-makers need to act to improve the environment for these patients. This paper also provides recommendations on how to reduce the noise levels, or at least control the noise on a maternity ward, through the implementation of a daily quiet time. DISCUSSION: Hospital disruptions and its negative health effects in particular for new mothers and their children are illustrated in this paper. Hospital noise and interruptions act as a stressor for both new mothers and staff, and can lead to sleep deprivation and detrimental cardiovascular health effects. Sleep deprivation is associated with a number of negative mental and physical health consequences such as decreased immune function, vascular dysfunction and increased sympathetic cardiovascular modulation. Sleep deprivation can also increase the risk of postpartum mental health disorders in new mothers. Some efforts have been made to reduce the disruptions experienced by these patients within a hospital setting. For example, the introduction of a daily quiet time is one way of controlling noise levels and interruptions, however, these have mostly been implemented in intensive care units. Noise and disruptions are a significant problem during postpartum hospital stay. Healthcare institutions are responsible for patient-centered care; a quiet time intervention promises to contribute to a safe, healing environment in hospitals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".