Factors associated with parents’ experiences using a knowledge translation tool for vaccination pain management: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Vaccination is a common painful procedure for children. Parents' concern regarding vaccination pain is a significant driver of vaccine hesitancy. Despite the wealth of evidence-based practices available for managing vaccination pain, parents lack knowledge of, and access to, these strategies. Knowledge translation (KT) tools can communicate evidence-based information to parents, however little is known about what factors influence parents' use of these tools. A two-page, electronic KT tool on psychological, physical, and pharmacological vaccination pain management strategies for children, was shared with parents as part of a larger mixed methods study, using explanatory sequential design, exploring factors related to uptake of this KT tool. The aim of this qualitative study was to understand what influenced parents' perceptions of the relevance of the KT tool, as well as their decision as to whether to use the tool. METHODS: A qualitative descriptive design was used. A total of 20 parents of children aged 0-17 years (n = 19 mothers) reviewed the KT tool ahead of their child's upcoming vaccination and participated in a semi-structured interview at follow-up. Interviews were recorded, transcribed verbatim, and analyzed with reflexive thematic analysis using an inductive approach. RESULTS: The analysis generated three interrelated themes which described factors related to parents' use of the KT tool: (1) Relevance to parents' needs and circumstances surrounding their child's vaccination; (2) Alignment with parents' personal values around, and experiences with, vaccination pain management (e.g., the importance of managing pain); and (3) Support from the clinical environment for implementing evidence-based strategies (e.g., physical clinical environment and quality of interactions with the health care provider). CONCLUSIONS: Several factors were identified as central to parents' use of the KT tool, including the information itself and the clinical environment. When the tool was perceived as relevant, aligned with parents' values, and was supported by health care providers, parents were more inclined to use the KT tool to manage their children's vaccination pain. Future research could explore other factors related to promoting engagement and uptake when creating parent-directed KT tools for a range of health-related contexts.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".