A Systematic Review of Knowledge Translation (KT) in Pediatric Pain
Bibliographic record
Abstract
OBJECTIVES: Pain is inadequately managed in pediatric populations across health care settings. Although training programs to improve health care provider knowledge and skills have been developed and evaluated, clinical practices have not always kept pace with advancing knowledge. Consequently, the goal of this review was to systematically examine the pediatric pain literature of knowledge translation (KT) programs targeting health care providers. MATERIALS AND METHODS: Systematic searches of PubMed, Web of Science, CINAHL, and PsycINFO were undertaken. KT initiatives directed toward health care providers and in which the primary focus was on pediatric pain were included. Primary outcomes, intervention characteristics, and risk of bias were examined across studies. Study outcomes were conceptually organized and a narrative synthesis of results was conducted. RESULTS: A total of 15,191 abstracts were screened for inclusion with 98 articles retained on the basis of predetermined criteria. Across studies, KT approaches varied widely in format and focus. Knowledge-level changes and self-reported increases in comfort or confidence in skills/knowledge were consistently achieved. Practice-level changes were achieved in many areas with varying success. Design and reporting issues were identified in the majority of studies. Examination of patient-related outcomes and of the long-term impact of pediatric pain KT programs was limited across studies. DISCUSSION: KT programs vary in quality and impact. Although several successful programs have been developed, many studies include a high risk of bias due to study quality. Evidence-based KT program implementation and a focus on sustainability of outcomes must be given greater consideration in the field of pediatric pain.
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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 | 0.028 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".