Seja Doce com os Bebês: avaliação de vídeo instrucional sobre manejo da dor neonatal por enfermeiros
Bibliographic record
Abstract
OBJECTIVE: To describe the profile of nurses who work in hospital units that care for newborns; to verify nurses' prior knowledge on breastfeeding, skin-to-skin care and sweet tasting solutions for neonatal procedural pain relief; and to evaluate nurses' perceptions on the feasibility, acceptability and usefulness of the Portuguese version of the "Be Sweet to Babies" video. METHOD: A cross-sectional study conducted in four units of a university affiliated hospital in São Paulo. Forty-five (45) nurses who answered the questionnaire and watched the video were included. Thirty-eight (38) nurses subsequently evaluated the video. Descriptive statistics were used to analyze the variables, in addition to content analysis of the open question. RESULTS: Forty-five (45) nurses participated in the study; 97.4% were aware of the analgesic strategies, and after watching the video nurses reported that they intend to use or encourage the use of these strategies during painful procedures. All participants would recommend the video to other professionals, and considered the resource as useful, easy to understand and easy to apply in real situations. CONCLUSION: Nurses are aware of the analgesic strategies and they considered the video as a feasible, acceptable and useful tool for knowledge translation to health care providers, which can also favor parental involvement in their children's pain management.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".