Comprehensive Overview of Preterm Developmental Supportive Care: Narrative Review
Bibliographic record
Abstract
Background: Preterm infants face significant health challenges due to the underdevelopment of their organ systems. The neonatal intensive care unit (NICU) is considered a stressful environment, which can disrupt self-regulation and maturation. Developmental supportive care strategies have been shown to enhance neurodevelopmental outcomes in neonates by mimicking intrauterine conditions, reducing stress, and providing appropriate sensory stimulation. Nursing interventions, particularly developmental care, are essential in improving neonatal health, with nurses playing a pivotal role in implementing these strategies. Aim: This article aimed to provide a comprehensive narrative synthesis of recent research on the impact of developmental supportive care on the health outcomes of preterm infants. Methods: An extensive literature search was conducted across PubMed, Scopus, Web of Science, Google Scholar, and the Cochrane Library. Quantitative studies, quasi-experimental research, observational studies, and meta-analyses of randomized clinical trials published in English within the past decade were included, with a particular emphasis on studies from the last five years. Results: The findings indicated that the application of developmental supportive care strategies in the neonatal intensive care unit significantly improved health outcomes for preterm infants, as reported in previous studies. Conclusion: This narrative overview demonstrated that developmental supportive care effectively contributes to the management and enhancement of preterm infants' health. The study recommends promoting and standardizing these practices in neonatal intensive care units, conducting a collective review of developmental supportive care techniques, and establishing training programs to improve health outcomes for preterm infants and identify effective interventions.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".