Maternal Perceptions of Feeding Behaviors in Infants With Neonatal Opioid Withdrawal Syndrome
Bibliographic record
Abstract
BACKGROUND: Neonatal opioid withdrawal syndrome (NOWS) significantly affects newborns, particularly when it comes to feeding. This underscores the need for comprehensive strategies to support growth, development, and the mother-infant bond. Despite the prevalence of NOWS, evidence-based feeding guidelines are scarce, and maternal perceptions of feeding behaviors remain underexplored, limiting tailored interventions. PURPOSE: Explore maternal perceptions of infant feeding behaviors in infants with NOWS. METHODS: This pilot, qualitative descriptive study used data from semi-structured interviews with eight women receiving treatment for opioid use during pregnancy. Interviews were conducted in community settings, transcribed, and analyzed through qualitative, latent content analysis. Initial coding in Excel identified subthemes, which were synthesized to establish the final themes. RESULTS: Most of the sample self-identified as Hispanic and were not working at the time. Analysis of participant accounts revealed the overarching theme, "Everything we do revolves around feeding." Sub-themes included: "Because all babies are different. Not all babies are the same," "It's good to know what to look out for," "It took a lot of learning," and "…There's all different kinds of problems in the NICU." These findings highlight maternal perceptions of infants with prenatal opioid exposure, emphasizing the role of feeding and the challenges in distinguishing normal behaviors from withdrawal symptoms. IMPLICATIONS FOR RESEARCH AND PRACTICE: The findings of this study highlight feeding as a crucial factor for growth and development in infants with NOWS, offering insights into interventions to address feeding challenges and improve outcomes for infants and families.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".