Adapting Child Health Knowledge Translation Tools for Somali Parents: Qualitative Study Exploring Process Considerations and Stakeholder Engagement
Bibliographic record
Abstract
BACKGROUND: We have developed a series of knowledge translation (KT) tools that integrate parental experiences to communicate evidence-based information about acute childhood health conditions to parents and caregivers. While we created these tools with parent input, it is unclear if they are useful for diverse parent groups, including specific immigrant and refugee groups in Canada. OBJECTIVE: This study aims to explore the usefulness of our preexisting KT tools within our local Somali community, and understand what cultural and linguistic adaptations could improve their usability. METHODS: After viewing 4 KT tools (differing in design and format) about various acute child health conditions, health care providers (HCPs) and knowledge brokers (KBs) who work with Somali families were interviewed about the usability of these tools and discussed considerations for adapting KT tools for use within the Somali community. RESULTS: A total of 13 HCPs and KBs participated and indicated that the Somali community values accessibility, representation, and the role of trusted others in delivering effective KT products. Understanding accessibility barriers, the power of adequate representation, and engaging meaningfully with prominent community leaders were key suggestions for ensuring relevance of KT products and uptake by community members. CONCLUSIONS: This study represents an essential piece of understanding processes for adapting or developing KT products for culturally and linguistically diverse communities.
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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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".