“Be sweet to babies”: Use of Facebook as a method of knowledge dissemination and data collection in the reduction of neonatal pain
Bibliographic record
Abstract
The Be Sweet to Babies video demonstrates the analgesic effects of breastfeeding, skin-to-skin care, and sweet-tasting solutions as interventions to reduce pain during blood sampling in newborns. Although effective and safe, these strategies are implemented inconsistently in clinical settings. Given the increasing popularity of social media, there is a potential to disseminate and promote health information through it. The study aim was to evaluate the use of Facebook as a means of disseminating the Be Sweet to Babies video in Portuguese, and to evaluate respondents' prior knowledge, previous use of the three pain management strategies and intent to use the strategies in the future. We conducted a cross-sectional study, using the "virtual snowball" sampling method. A Facebook webpage was created, in which the video was posted along with a brief survey. Data analyzed included number of views and visits to the page, number of views of the video, likes, dislikes, and survey responses. One year after posting, the page had 70 753 views and 2199 accesses; there were 1553 "likes", no dislikes, and 43 positive comments. The survey was completed by 930 respondents (42% response rate based on the page access). Over two thirds of the respondents had previous knowledge about breastfeeding, skin-to-skin care, and sweet solutions for pain relief. After watching the video, 87% of the respondents intended to use breastfeeding or skin-to-skin care in the future, and 71% intended to use sweet solutions. Almost all viewers rated the video as very useful (n = 917, 99%), easy to understand (n = 926, 99%), and easy to apply in real-life situations (n = 903, 97%). Using Facebook to deliver and evaluate an intervention is feasible, rapid in obtaining responses, low cost, and it is promising for data collection and knowledge dissemination. Further studies are warranted to evaluate the actual impact of the use of social media in practice change.
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".