Introducing a Quiet Time on a Maternity Ward: Engaging Patients and Staff to Assess Benefits and Barriers
Bibliographic record
Abstract
BackgroundA postpartum hospital stay should provide new mothers an environment conducive to resting and healing.However, these patients often experience disruptions from activities related to visitors, clinical care, hospital services, and intercom announcements.This can lead to potential interruptions in important activities such as breastfeeding and teaching, and can increase the risk of postpartum mental health problems.A possible solution is a quiet time, a period of time where lights are dimmed, potential interruptions are reduced, and routine care processes are scheduled outside of this time as possible.However, only one unpublished study related to such an initiative on a maternity ward was found.The goal of this study is to contribute to the general pool of knowledge regarding noise levels and number of potential interruptions on a maternity ward along with an understanding of the benefits, barriers and implementation issues associated with the introduction of a quiet time on a maternity ward. Methods This study took place on a maternity ward in a community general hospital in Montreal.A mixed methods research design was adopted within a larger pre-post evaluation involving a participatory research approach.Noise levels were measured via Decibel 10 th iPhone app; potential interruptions were noted by observation.A modified version of the Canadian Patient Experiences Survey (CPES) was distributed to inpatients.Qualitative interviews were conducted with postpartum mothers and observation notes were recorded at stakeholder meetings.An average mean A-weighted equivalent sound level (Leq) was computed, and average minimum and average maximum decibel levels were calculated and identified.We performed
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".