Pain in hospitalized children: Effect of a multidimensional knowledge translation strategy on pain process and clinical outcomes
Bibliographic record
Abstract
Hospitalized children frequently receive inadequate pain assessment and management despite substantial evidence to support effective pediatric pain practices. The objective of this study was to determine the effect of a multidimensional knowledge translation intervention, Evidence-based Practice for Improving Quality (EPIQ), on procedural pain practices and clinical outcomes for children hospitalized in medical, surgical and critical care units. A prospective cohort study compared 16 interventions using EPIQ and 16 standard care (SC) units in 8 Canadian pediatric hospitals. Chart reviews at baseline (time 1) and intervention completion (time 2) determined the nature and frequency of painful procedures and of pain assessment and pain management practices. Trained pain experts evaluated pain intensity 6 months post-intervention (time 3) during routine, scheduled painful procedures. Generalized estimating equation models compared changes in outcomes between EPIQ and SC units over time. EPIQ units used significantly more validated pain assessment tools (P<0.001) and had a greater proportion of patients who received analgesics (P=0.03) and physical pain management strategies (P=0.02). Mean pain intensity scores were significantly lower in the EPIQ group (P=0.03). Comparisons of moderate (4-6/10) and severe (7-10/10) pain, controlling for child and unit level factors, indicated that the odds of having severe pain were 51% less for children in the EPIQ group (adjusted OR: 0.49, 95% CI: 0.26-0.83; P=0.009). EPIQ was effective in improving practice and clinical outcomes for hospitalized children. Additional exploration of the influence of contextual factors on research use in hospital settings is required to explain the variability in pain processes and clinical outcomes.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".