FACTORS PREDICTING NURSES’ USE OF EVIDENCE TO REDUCE PROCEDURAL PAIN IN NEONATES
Bibliographic record
Abstract
Objective We examined the effects of nurse, infant and organisational factors on the delivery of higher pain care by neonatal intensive care nurses. Methods We included 93 nurses from two neonatal intensive care units who had performed 170 pain-producing procedures. Nurse use of evidence-based protocols to manage procedure-related pain using a scorecard of nurses’ assessment, management and documentation were examined in the context of infant acuity, nurse physician collaboration and nurse workload. Nurse knowledge of pain care was measured using a newly developed pain knowledge and use instrument with good psychometric properties. Results Procedural pain care was more likely to meet evidence-based criteria when nurses rated nurse–doctor collaboration more highly (odds ratio (OR) 1.44; 95% CI 1.05 to 1.98), when infants required higher intensity care (OR 1.21; 95% CI 1.06 to 1.39) and when treating nurses experienced unexpected increases in their work assignments (OR 1.55; 95% CI 1.04 to 2.30). Nurses’ knowledge levels about the protocols, educational preparation and their experience were not significant predictors of implementation of evidence-based care. Conclusion Organisational factors such as nurse–physician collaboration and work assignments were more predictive of evidence-based care than nurse factors. Nurses’ knowledge levels regarding evidence-based care were not a predictor of the implementation of protocols. In the final modelling, collaboration with physicians, a variable amenable to intervention and further study, emerged as a strong predictor. The results highlight the complex issue of translating knowledge to practice; however, specific findings related to pain assessment and collaboration provide some direction for future practice and research initiatives.
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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 | 0.010 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".