Factors Influencing Mothers’ Quality of Sleep during Their Infants’ NICU Hospitalization
Bibliographic record
Abstract
Study Objectives The purpose of this study is to identify factors associated with Neonatal Intensive Care Unit (NICU) mothers’ quality of sleep as measured with the General Sleep Disturbance Scale (GSDS).Methods Recruitment took place in a level 3 NICU. At enrollment, mothers completed a socio-demographic questionnaire, described their presence in the unit and their breast milk expression behavior. They also completed online or paper questionnaires about NICU-related stress, symptoms of postpartum depression, family-centered care, perception of noise and light in the unit, and sleep disturbances. Data regarding the infant’s clinical condition were collected from their medical file. Pearson correlations were performed to identify associations between mothers’ quality of sleep and other study variables. Subsequently, to compare mothers with a clinically significant GSDS score to mothers with a non-significant score, a binary logistic regression model was conducted.Results 132 mothers participated. Sleep disturbances of mothers with an infant hospitalized in the NICU was positively correlated with stress (r = 0.40; p = .00), depressive symptoms (r = 0.51; p = .00), and breast milk expression (r = 0.23; p = .01). In addition, for mothers with significant levels of depressive symptoms (OR = 1.19; p = .00), with greater presence in the unit (OR = 1.36; p = .04), or with other children at home (OR = 3.12; p = .04), the likelihood of clinically significant sleep disturbances was increased.Conclusions These results improve our understanding of the factors influencing the quality of sleep of mothers whose premature infant is hospitalized for 2 weeks or more in the NICU. In addition, these results allow the identification of mothers having a higher possibility for sleep disturbance, which enables the implementation of targeted interventions to promote adequate sleep.
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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 | 0.000 | 0.004 |
| 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) | 0.001 | 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".