Implementation of a new clinical practice guideline regarding pain management during childhood vaccine injections
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of a multifaceted knowledge translation strategy for a new vaccination pain management guideline on public health immunizers' attitudes, beliefs and use of pain-relieving strategies during childhood vaccination. METHOD: Using a randomized controlled pre-post study design, public health nurses (PHNs) at intervention sites received a multifaceted knowledge translation intervention about new pain management guidelines incorporated in the British Columbia Immunization Program Manual, including education, supplies and online support. Attitudes and beliefs of PHNs toward immunization pain and pain management, and use of pain-relieving strategies were compared for the intervention sites between the pre- and postimplementation phases. RESULTS: A total of 516 children were immunized by 31 PHNs pre- and postimplementation in the intervention sites. Postimplementation, satisfaction and confidence with ability to manage pain and willingness to use newly recommended strategies were significantly more positive (P<0.05) in the intervention sites, and overall use of at least one newly recommended strategy increased from 49.8% preintervention to 77.6% postimplementation (P<0.001). CONCLUSION: The knowledge translation intervention improved PHN immunizers' attitudes, beliefs and practices regarding paediatric vaccination pain management. Reducing pain may result in a better immunization experience for children, caregivers and immunizers.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".