Exploring implementation processes of a parent-targeted educational video for improving newborn pain treatment: A sequential exploratory mixed-methods study
Bibliographic record
Abstract
Despite known analgesic effects of breastfeeding (BF), skin-to-skin care (SSC), and sweet solutions (sucrose) for newborns, these interventions remain underutilized. Our team produced a five-minute parent-targeted video (BSweet2Babies) demonstrating BF, SSC, and sucrose during newborn blood sampling. We conducted a sequential exploratory mixed-methods study with eight maternal-newborn units across Ontario, Canada to identify barriers and facilitators to implementing the video and the three pain management strategies. Over a 6-month period, data collection included 15 telephone interviews, two email communications, and three community of practice teleconferences with the participating sites ( n = 8). We used the Theoretical Domains Framework as the coding matrix. Participants discussed integrating the video in prenatal education and the importance of involving leadership when planning for practice change. Key barriers included lack of comfort with parental presence, perception of high complexity of the strategies, short postpartum stays, competing priorities, and interprofessional challenges. Key facilitators included alignment with the Baby-Friendly Hospital Initiative, modeling by Lactation Consultants, and frequent reminders.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".