Parent-led neonatal pain management—a narrative review and update of research and practices
Bibliographic record
Abstract
Introduction: Research related to parent-led neonatal pain management is increasing, as is the clinical implementation. Skin-to-skin contact, breastfeeding and parents' vocalizations are examples of pain reducing methods that give parents an opportunity to protect their infant from harm while alleviating their anxiety and developing their parenting skills. Methods: In this paper we will provide a narrative review and describe the current research about parent-led neonatal pain management. Based on this we will discuss clinical challenges, implementation strategies and implications for future research. Results: Parents express great readiness to embrace opportunities to increase their self-efficacy in their ability to address infant pain. Parent-led pain-reducing methods are effective, feasible, cost-effective, culturally sensitive, and can be individualized and tailored to both the parent's and infant's needs. Both barriers and facilitators of parent-led pain care have been studied in research highlighting structural, organizational, educational, and intra- and interpersonal aspects. For example, health care professionals' attitudes and beliefs on parent-led methods, and their concern that parental presence during a procedure increases staff anxiety. On the other hand, the presence of a local pain champion whose duty is to facilitate the adoption of pain control measures and actively promote parent-professional collaboration, is crucial for culture change in neonatal pain management and nurses have a key role in this change. The knowledge-to-practice gap in parent-led management of infants' procedure-related pain highlight the need for broader educational applications and collaborative professional, parental and research initiatives to facilitate practice change. Conclusion: Parent-led neonatal pain management is more than simply a humane and compassionate thing to do. The inclusion of parent-led pain care has been scientifically proven to be one of the most effective ways to reduce pain associated with repeated painful procedures in early life and parents report a desire to participate. Focus on enablers across interprofessional, organizational and structural levels and implementation of recommended pediatric pain guidelines can support the provision of optimal evidence-based family-centered neonatal pain management.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".