Pain Assessment and Management After a Knowledge Translation Booster Intervention
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Inadequate pain treatment leaves hospitalized children vulnerable to immediate and long-term sequelae. A multidimensional knowledge translation intervention (ie, the Evidence-based Practice for Improving Quality [EPIQ]) improved pain assessment, management, and intensity outcomes in 16 units at 8 Canadian pediatric hospitals. The sustained effectiveness of EPIQ over time is unknown, however. The goals of this study were to determine the following: (1) sustainability of the impact of EPIQ on pain assessment, management, and intensity outcomes 12, 24, and 36 months after EPIQ; (2) effectiveness of a pain practice change booster (Booster) intervention to sustain EPIQ outcomes over time; and (3) influence of context on sustainability. METHODS: A prospective, repeated measures, cluster randomized controlled trial was undertaken in the 16 EPIQ units, 12 months after EPIQ completion, to determine the effectiveness of a practice change booster (Booster) to sustain EPIQ outcomes. Generalized estimating equation models examined outcomes controlling for child and unit contextual factors. RESULTS: Outcomes achieved during EPIQ were sustained in the use of any pain assessment measure (P = .01) and a validated pain assessment measure in the EPIQ units (P = .02) up to 36 months after EPIQ. Statistically significant improvements in pain management practices persisted in EPIQ units; results varied across time. There were no significant differences in outcomes after implementation of the Booster between the Booster and Nonbooster groups. CONCLUSIONS: Improved pain assessment and management practices were sustained after EPIQ; however, the Booster did not seem to provide additional impact.
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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 | 0.005 | 0.010 |
| 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 | 0.002 |
| 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".