Exploring cultural adaptation elements in child health knowledge translation tools for parents: A scoping review
Bibliographic record
Abstract
The aim of this scoping review was to identify and map available evidence on cultural adaptation elements incorporated into child health knowledge translation (KT) tools for parents. A search of eight databases was conducted (2001-2024). Two reviewers worked independently for screening, study selection, and data extraction. Extracted data included number and type of cultural adaptations, and approach taken to performing cultural adaptation. Studies were then categorized and mapped by these attributes, and sub-categories emerged based on patterns of occurrence between included studies. Of 3946 unique articles, 20 met the inclusion criteria. Three main types of cultural adaptation elements were described: (a) language, (b) visual representations, and (c) cultural values. The most common child health conditions of included studies were autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), and asthma. Further exploration of cultural values and their inclusion in KT tools is needed to meet the information needs of culturally and linguistically diverse (CALD) families. The findings from this review underscore the necessity for further research to explore cultural adaptation processes required to develop child health KT tools to assist clinicians and provide more targeted, culturally relevant support for CALD parents.
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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 | 0.047 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".