A Quality Improvement Project to Increase Frequency of Skin-to-Skin Contact for Extreme Low-Birth-Weight Infants in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
Benefits of skin-to-skin contact (SSC) are documented but often delayed in the extremely preterm population due to medical complexity and staff misconceptions about safety. This quality improvement initiative was designed to increase SSC utilization among infants born before 29 weeks' gestation regardless of respiratory support by addressing nursing barriers inhibiting SSC. A pre-/postsurvey evaluated comfort level performing and perceived barriers to SSC utilization. Implementation consisted of an updated unit-specific SSC protocol and tailored education specific to identified barriers. Evaluation included SSC rates and maternal human milk usage in the first 30 days of life. In total, 81 patients (22-28 weeks, 370-1410 g) were included. SSC rates ranged from 3.3% to 17.7% at baseline and increased to 33.2% to 39.1% postintervention. Maternal human milk utilization increased above target (≥75%) postintervention for days 7 and 14, but declined towards baseline on days 21 and 30. A statistically significant increase was observed in nursing comfort level when performing SSC for intubated infants as well as infants with a peripherally inserted central catheter or umbilical venous catheter. SSC rates increased with infants younger than 29 weeks requiring intubation and central line management, possibly as a result of greater nursing comfort surrounding with SSC.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".