Exploring the creation or adaptation of knowledge mobilization products for culturally and linguistically diverse audiences: a scoping review
Bibliographic record
Abstract
Abstract Introduction Connecting end-users to research evidence has the power to improve patient knowledge and inform health decision-making. However, recognized barriers to or determinants of effective knowledge mobilization (KMb) are differences in culture and language among the end users of the evidence. This scoping review set out to understand current processes and practices when creating or adapting KMb products for culturally and linguistically diverse (CALD) audiences. Methods We searched 3 databases (Ovid Medline, CINAHL via EBSCOhost, PsychINFO) from 2011 until August 2021. We included any literature about KMb product creation or adaptation processes serving CALD communities. A primary reviewer screened all identified publications and a second reviewer screened publications excluded by the primary. Data were extracted using a standardized form by one reviewer and 10% were verified by a second reviewer. Studies were categorized by type of adaptation (‘surface’ or ‘deep’ structure) and mapped based on type of stakeholder engagement used (i2S model). A search update was run in July 2023, and screening and extraction are in progress. Results Seven thousand four hundred and five unique titles and abstracts were reviewed, 319 full-text studies were retrieved and reviewed, and 24 studies were included in final data extraction and mapping. Fifteen studies (63%) created or adapted exclusively text-based KMb products such as leaflets and pamphlets and 9 (38%) produced digital products such as videos (n=4, 16%), mobile applications (n=3, 13%), website (n=1, 4%) and a CD ROM (n=1, 4%). Eight studies (33%) reported following a framework or theory for their creation or adaptation efforts. Only five studies (21%) demonstrated ‘deep structure’ cultural sensitivity and applied all five (Inform, Consult, Involve, Collaborate, and Support) levels of stakeholder engagement. Four (17%) studies included reflections from the research teams on the processes for creating or adapting KMb products for CALD communities. Conclusion Included studies cited a variety of methods in creating or adapting KMb products for CALD communities. Successful uptake of created or adapted KMb products was often the result of collaboration with end-users for more applicable, accessible and meaningful products. Further research developing guidance and best practices is needed to support the creation or adaptation of KMb products with CALD 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 | 0.049 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".