Why are children still crying? Going beyond “evidence” in guideline development to improve pain care for children
Bibliographic record
Abstract
The failure to translate research evidence into day-to-day clinical practices is identified as a significant reason for suboptimal quality care across the health system, including procedural pain management in children. Clinical practice guidelines (CPGs) have been developed to assist in this process by synthesizing and interpreting research evidence for end users. Numerous CPGs have been developed for procedural pain management in children, yet gaps persist in the adoption of best practices. This article reviews the experience and approach of 1 guideline development group, the Help ELiminate Pain in KIDS Team (HELPinKIDS), in incorporating implementation considerations and knowledge translation (KT) strategies within the process of guideline development for the HELPinKIDS CPG about childhood vaccination pain management to facilitate greater uptake of the CPG. Specific areas that will be addressed include partnerships with stakeholders, rigor of guideline development, issues of implementation, and editorial independence. The work of HELPinKIDS was guided by a KT map, which identified, at a high level, the target audiences, key messages, tools, and strategies that could be used to communicate, disseminate, and implement the CPG into diverse settings. Examples of impact at both the individual and systems levels from HELPinKIDS KT activities are also presented.
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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 | 0.031 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".