Be Sweet to Babies During Painful Procedures
Bibliographic record
Abstract
BACKGROUND: Breastfeeding (BF), skin-to-skin care (SSC), and sucrose effectively reduce babies' pain during newborn blood work, but these strategies are infrequently used. Our team developed a parent-targeted video intervention showing the effectiveness of the 3 pain management strategies. PURPOSE: To evaluate neonatal intensive care unit (NICU) parents' (1) baseline knowledge and previous use of BF, SSC, and sucrose for procedural pain management; (2) intention to advocate/use BF, SSC, or sucrose for their infants' future blood work after viewing the video; (3) intention to recommend the video to other parents; and (4) perceptions of the video and identify areas for improvement. METHODS: Cross-sectional survey of parents in an NICU. RESULTS: Fifty parents were enrolled: 33 mothers and 17 fathers. More than two-thirds (68%) of parents had prior knowledge of analgesic effects of sucrose; knowledge of SSC and BF as pain-reduction strategies was lower: 44% and 34%, respectively. Eighty-six percent of parents felt the video was the right length; 7 (14%) felt the video was too long. After viewing the video, 96% of parents intended to advocate for BF, SSC, or sucrose for pain management and 88% parents would recommend the video to other parents. IMPLICATIONS FOR PRACTICE: The video is acceptable to parents, is feasible to deliver to parents in an NICU, and has potential to increase parents' intent to advocate for pain management strategies for their infants. IMPLICATIONS FOR RESEARCH: Future studies are required to evaluate the effectiveness of this parent-targeted intervention on increasing actual use of pain management in clinical practice.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".