Advances in family-based interventions in the neonatal ICU
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite advances in medical care, preterm infants remain at risk for many adverse outcomes. This article reviews findings from several recent neonatal ICU (NICU) interventions and a trial of a novel nurture-based approach, Family Nurture Intervention (FNI). RECENT FINDINGS: Recent trials reviewed here find positive effects of a variety of family-related interventions focused on parental guidance. These interventions target prescribed physical activities with infants, parents' stress, and the parents' ability to recognize their positive and negative behaviors with their infants. Beneficial effects include reductions in parenting stress, maternal anxiety, and depression. A different approach, FNI, is aimed at establishing mother-infant emotional connection. As in other trials, FNI also decreased maternal symptoms of anxiety and depression, and increased maternal sensitivity. Additionally, FNI led to positive short and long-term effects on infant neurobehavioral outcomes at term and 18 months. SUMMARY: A number of recent parent-based NICU interventions have been effective at reducing preterm parent stress. Another, FNI, has positive effects on both maternal and infant outcomes and promises to be cost-effective. Future decreases in long-term morbidity in preterm infants will increasingly rely on nonmedical interventions. Therefore, the rigorous development and testing of such interventions should be a high priority in perinatology research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".