53 Pediatric Pain: A Network's Approach to Education
Bibliographic record
Abstract
The Child Health Network for the Greater Toronto Area (CHN) is a partnership among 20 hospitals that provide maternal/newborn and paediatric services, and 10 Community Care Access Centres that manage home-based services. Using pain management education, this article explores whether a network approach to education has merit and influences practice changes. An Education Framework was developed to promote and support educational initiatives across the network. Pain management was identified as a hospital priority, whereby improvements were needed in clinicians' awareness, understanding and clinical practices about procedural, post-operative, peri-operative and traumatic pain in neonates, infants, children and youth. Best practice standards and education modules were developed on paediatric pain assessment and management, and a train-the-trainer approach was used for education. CHN's paediatric pain management initiative had positive impacts. Changes in clinical practice were evident in 10 out of 12 hospitals. Eight hospitals instituted developmentally appropriate pain assessment tools for children, seven hospitals for youth, one hospital for neonates and one for infants. As a network, the CHN hospital collective worked collaboratively to develop best practice standards, and a methodological and comprehensive education approach. Resource constraints, lack of buy-in and competing priorities impacted on more wide-scale implementation of the pain management standards and best practices. Networks can play an important role influencing change and promoting best practice standards. CHN's pain management initiative suggests that a network approach to education has definite merit and can influence changes in practice.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".