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Record W4210748161 · doi:10.2196/33156

Perspectives From French and Filipino Parents on the Adaptation of Child Health Knowledge Translation Tools: Qualitative Exploration

2022· article· en· W4210748161 on OpenAlexaffvenue
Sarah A Elliott, Kelsey S Wright, Shannon D. Scott, Lisa Hartling

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCochraneUniversity of Alberta
Fundersnot available
KeywordsTagalogUsabilityPsychologyThematic analysisAdaptation (eye)Knowledge translationNarrativeRelevance (law)Qualitative researchMedical educationComputer scienceLinguisticsMedicineKnowledge managementSociology

Abstract

fetched live from OpenAlex

BACKGROUND: A number of evidence-based knowledge translation (KT) tools for parents of children with acute health conditions have been developed. These tools were created and tested with parental input and disseminated to groups proficient in English. Therefore, it is unclear whether they are useful for populations that are more diverse. To enhance the reach of our current and future KT tools, language translation and cultural adaptations may promote relevance for previously underserved knowledge users. OBJECTIVE: This study aims to explore and understand considerations for the cultural and linguistic adaptation of a KT tool in French and Filipino communities. METHODS: A KT tool (whiteboard animation video) describing the signs and symptoms of croup was originally developed in English to provide parents with evidence-based information couched within a narrative reflecting parents' experiences with the condition. This KT tool was adapted (linguistics and imagery) for French- and Tagalog-speaking parents and caregivers through feedback from key stakeholders. The videos were presented to the respective language speakers for usability testing and discussion. Participants were asked to view the KT tool, complete a usability survey, and participate in semistructured interviews. Audio recordings from the interviews were transcribed verbatim, translated into English, and analyzed for relevant themes by using thematic analysis. RESULTS: French- (n=13) and Tagalog-speaking (n=13) parents completed the usability survey and were interviewed. Although analyzed separately, both data sets produced similar findings, with key themes relating to understanding, relatability, and accessibility. Both the French and Tagalog groups reported that the video and other KT tools were useful in their adapted forms. Participants in both groups cautioned against using verbatim vocabulary and suggested that cultural competency and understanding of health languages were essential for high-quality translations. Parents also discussed their preference for videos with diverse visual representations of families, home environments, and health care workers, as such videos represent their communities more broadly. CONCLUSIONS: French and Filipino parents appreciated having KT tools in their first language; however, they were also supportive of the use of English KT products. Their suggestions for improving the relatability and communication of health messages are important considerations for the development and adaptation of future KT products. Understanding the needs of the intended end users is a crucial first step in producing relevant tools for health evidence dissemination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.007
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.862
GPT teacher head0.724
Teacher spread0.139 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2022
Admission routes2
Has abstractyes

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