A Parent-Targeted and Mediated Video Intervention to Improve Uptake of Pain Treatment for Infants During Newborn Screening
Bibliographic record
Abstract
Most newborns undergo newborn screening blood tests. Breastfeeding, skin-to-skin care, and sweet solutions effectively reduce pain; however, these strategies are inconsistently used. We conducted a 2-armed pilot randomized controlled trial in a mother-baby unit to examine the feasibility and acceptability of a parent-targeted and -mediated video demonstrating use of these pain-reducing strategies and to obtain preliminary effectiveness data on uptake of pain management. One hundred parent-newborn dyads were randomized to view the video or receive usual care (51 intervention and 49 control arm). Consent and attrition rates were 70% and 1%, respectively. All participants in the intervention arm received the intervention as planned and reported an intention to recommend the video and to use at least 1 pain treatment with breastfeeding or skin-to-skin care preferred over sucrose. In the intervention arm, 60% of newborns received at least 1 pain treatment compared with 67% in the control arm (absolute difference, -7%; 95% confidence interval, -26 to 12). The video was well accepted and feasible to show to parents. As there was no evidence of effect on the use of pain management, major modifications are required before launching a full-scale trial. Effective means to translate evidence-based pain knowledge is warranted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".