Effects of eye cover among high risk neonates at night shift on their distress levels
Bibliographic record
Abstract
Background and aim: The Neonatal Intensive Care Unit (NICU) is a stressful environment for high risk neonates. Persistent bright light is one of the main environmental stressors that are distressed newborn infants in NICU. Cycled lighting may decrease distress level of newborn infants by enhancing calming status. This study aimed to investigate effects of eye cover among high risk neonates at night shift on their distress levels.Methods: Quasi experimental research design was carried out on a randomized sample of 60 newborn infants attending the NICU of El Manial University Hospital (Kasr Al Ainy), (30 control group and 30 study group). Neonatal assessment tool and COMFORTneoNRS scale were utilized for data collection.Results: There was a statistically significant difference between control and study groups regarding the distress levels (p < .00). The mean score of distress levels were 6.80 ± 1.80 and 0.80 ± 1.15 respectively and the mean score of comfort levels in the newborn infants in the control and study groups were 23.22 ± 5.50 and 6.60 ± 1.06 respectively. Eye coved enhanced quite sleep (66.7%), relaxed muscle (73.3%), decrease movement (66.7%) and no crying (85.7%).Conclusions: The use of eye cover among high risk neonates at night shift is effective to decrease their distress level and improve their comfort state in the morning shift by promoting quite sleep and relaxation. Recommendations: The educational program is needed to raise awareness among neonatal nurses about the effect of light reduction methods such as eye patches on the distress level and comfort state that enhances the growth and development of newborn infants.
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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".