Nurses' Perception of Preterm Infants' Pain and the Factors of Their Pain Assessment and Management
Bibliographic record
Abstract
In the neonatal intensive care unit, preterm infants undergo many painful procedures. Although these can impair their neurodevelopment if not properly managed, only half of the painful procedures are optimally handled. This cross-sectional study aimed to evaluate nurses' perceptions of preterm infants' pain, to evaluate nurses' pain assessment and management practices, as well as to identify the individual and contextual factors that influence nurses' assessments and interventions for pain management. Secondary analyses, including a mixed-model analysis, were performed with data from a larger study (n = 202 nurses). Nurses were found to have attitudes and perceptions in favor of preterm infants' pain management, although they reported using few standardized instruments to assess pain. Nurses stated that they widely used sucrose, non-nutritive sucking, and positioning as pain management interventions, while skin-to-skin contact was rarely practiced. Nurses' attitudes and perceptions influenced their pain assessment practices, which predicted their implementation of interventions. Several contextual (country, level of care, and work shift) and individual factors (age, level of education, had a preterm infant, perceptions of family-centered care, and skin-to-skin contact) also predicted nurses' pain assessment and management practices.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".