Noise pollution in a tertiary hospital and the impact on patient health and staff productivity
Bibliographic record
Abstract
Background: Excessive noise production is a daily feature in many parts of the world, noise has also crept into hospital environments. Many factors are responsible for noise pollution leading to growing concern over high noise levels in hospitals and the effect on patients' health and staff's productivity. Objective: This study sought to determine the hospital personnel's perceptions on the effect of noise on productivity, and patients' perceptions on noise levels within a large tertiary hospital. Methodology: Focus Group Discussions (FGD) and Key In-depth Interviews (KII) were conducted to generate themes/issues/questions from hospital personnel and staff/relations, respectively. Participants in the FGD were selected from the wards using a multistage sampling technique. Stratified random sampling was used to select participants for KII. The FGD and KII sessions were transcribed, and a codebook was developed with broad codes, fine codes, and their definitions to reflect and describe the resulting themes. A thematic approach was used for the analysis. Results: Hospital sources of noise were grouped into two categories: noise originating within the wards, such as staff conversation and equipment like monitors, mechanical ventilators, and noise originating outside the wards, such as electricity generating sets and engineering works. Noise was identified as a disruptor of communication and concentration, leading to reduced productivity. Noise pollution also affected patients' health by causing headaches and sleeplessness. Conclusion: Noise pollution within hospitals emanates from diverse sources, reducing staff productivity and impairing patients' health. A multi-pronged approach is required to reduce hospital noise pollution
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".