Implementation of a knowledge translation and exchange intervention for pain management in neonates
Bibliographic record
Abstract
Objective To describe the implementation process of a multifaceted knowledge translation and exchange intervention to improve pain management practices, and to evaluate the adoption of this intervention by health professionals during painful procedures in neonates. Methods A quasi-experimental before-and-after study developed in a neonatal unit. The Evidence-Based Practice for Improving Quality intervention guided by the conceptual framework The Promoting Action on Research Implementation in Health Services was implemented in two stages (preparation and implementation). Its adoption was measured by clinical indicators related to pain management presented through descriptive statistics. Results After discussion on existing practices in the unit that needed to be changed, synthesis of current scientific evidence and local context data, members of the unit’s Research and Practice Council developed and implemented coherent and achievable goals for the change of practice in pain management, selected knowledge translation and exchange strategies, determined the target audience and indicators, and implemented the interventions. There was a 32.8% reduction in the number of painful procedures performed, an increase of 26.6-50.7% in the use of pain assessment scales and of 25.1% in the administration of oral glucose. Conclusion The multifaceted Evidence-Based Practice for Improving Quality intervention is complex, and has processes that demand knowledge and skills, commitment from the various actors involved, availability of time and financial investment. The analyzed indicators showed that the intervention resulted in positive changes in clinical practice in the management of pain in neonates.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".