Increasing the Use of Newborn Pain Treatment Following the Implementation of a Parent-Targeted Video: An Outcome Evaluation
Bibliographic record
Abstract
Background/Objectives: Despite strong evidence that breastfeeding, skin-to-skin care, and sucrose reduce pain in newborns during minor painful procedures, these interventions remain underutilized in practice. To address this knowledge-to-practice gap, we produced a five-minute parent-targeted video demonstrating the analgesic effects of these strategies and examined whether the use of newborn pain treatment increased in maternal–newborn care settings following the introduction of the video by nurses. Methods: The design was a pre–post outcome evaluation. The participants were infants born in eight maternal–newborn hospital units in Ontario, Canada. Data on newborn pain treatment were obtained from a provincial birth registry. Descriptive statistics and chi square tests were used to compare the before-and-after changes in the use of pain treatment. Results: Data on 15,524 infants were included. Overall, there was an increase in the proportion of newborns receiving any pain treatment comparing before (49%) and after (54%) the video intervention (p < 0.0001) and a decrease in the proportion of newborns receiving no pain treatment pre- (17.6%) and post-intervention (11.5%) (p < 0.0001). Most of the change aligned with increased sucrose use (35% to 47%, p < 0.0001) in three of the larger units. Nevertheless, considerable increases in the use of breastfeeding and/or skin-to-skin care (24% to 38%, p < 0.0001) were also observed in three of the smaller units. Conclusions: The video intervention was effective at increasing the use of pain treatment for newborns. Though the overall increases were modest, there were some large increases for specific methods of pain treatment in certain maternal–newborn units, reflecting the diversity in practice and context across different sites.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".