Understanding parents' use of a knowledge translation tool to manage children's vaccination pain
Bibliographic record
Abstract
INTRODUCTION: Although several evidence-based strategies for managing children's vaccination pain exist, many parents report being unaware of them. Knowledge translation (KT) tools present evidence-based information in plain language. OBJECTIVES: This two-phase study assessed parents/caregivers' uptake of evidence-based pain management strategies via a KT tool and considered factors related to parents' planned, actual, and future use of these strategies. METHODS: In phase 1, parents were exposed to an online KT tool on physical, psychological, and pharmacological vaccination pain management strategies, and their impressions were assessed by questionnaires including the Information Assessment Method for Parents. In phase 2, after vaccination, parents completed a follow-up survey on their uptake and experiences using the information. RESULTS: A total of 312 participants reported their plans for KT tool use. Parents who found the KT tool relevant were more likely to plan to use it at their child's upcoming vaccination. A total of 128 parents (93% mothers) completed both surveys. Nearly all parents who planned to use the information did so during their child's subsequent vaccination (90%). When the KT tool was relevant to their needs, parents were more likely to use the information during their child's vaccination. Parents who felt confident using the tool were significantly more likely to report plans for future tool use. DISCUSSION: This study demonstrates the effectiveness of a KT tool that was relevant to parents' needs and built confidence to increase parent-reported uptake of evidence-based strategies. Proper pain management could positively impact parents' uptake of vaccinations for children.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".