Understanding maternal perspectives of skin-to-skin contact for the management of acute pain in very and extremely preterm infants
Bibliographic record
Abstract
ABSTRACT: Skin-to-skin care (SSC) and skin-to-skin contact for procedural pain (SSCP) are recognized for their physiological and emotional benefits in the neonatal intensive care unit (NICU), including pain reduction in preterm infants. However, little is known about how birthing parents of very and extremely preterm infants (<32 weeks gestational age), a significantly more challenging preterm infant population to enact SSCP, perceive this intervention. This study aimed to explore birthing parents' experiences and perceptions related to the use of SSC and SSCP in the NICU with their very and extremely preterm infants. In partnership with a national preterm parent organization, virtual interviews were conducted with 38 mothers of very or extremely preterm infants from across Canada, who had been admitted to the NICU within the past 5 years. Data were synthesized into 8 primary themes relating first to SSC broadly and then SSCP. In addition, mothers' opinions about a priori concepts and potential interventions (generated from pilot data) were also vetted. Important actionable facilitators and barriers related to fears and interventions to support SSCP with parents of very and extremely preterm infants were discerned. Although most found their experience rewarding, barriers such as limited instruction, inconsistent staff support, procedural challenges, and emotional strain often hindered the use of SSCP. Enhancing staff training, standardizing protocols, offering mental health support, and adopting flexible, family-centered policies appear key to improving SSCP engagement with the youngest preterm infants.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 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".