Translating/Creating a Culturally Responsive Spanish-Language Mobile App for Visit Preparation: Case Study of “Trans-Creation”
Bibliographic record
Abstract
BACKGROUND: Health information technology (IT) tools are increasingly used to improve patient care. However, implementation of English-only health IT tools could potentially worsen health disparities for non-English speakers. OBJECTIVE: We aim to describe the "trans-creation" process of developing linguistically and culturally appropriate health IT tools through a detailed case analysis of a waiting room health mobile app designed to help Spanish-speaking Latino people prepare for primary care visits. METHODS: We adapted the English-language Visit Planner mobile app for Spanish-speaking Latino patients. We applied culturally defined themes derived from prior published research and input by both skilled linguists and potential end users. Initial changes were iteratively reviewed and edited by a team of writers, health care educators, subject matter experts, patients, and providers. RESULTS: The trans-creation process resulted in the following key culturally mediated changes to the tool: replacing the "provider" actors with "patient" actors; changing the choice of "Stress at Home or Work" (represented by an icon of a house) to "Mi Familia" (translation: my family; icon is an outline of family members holding hands); replacing the English terms "anxiety" and "depression" with "Me siento desanimado"(translation: I am feeling down) to avoid mental health stigma; and using more concise text translation to ensure the wording fit the available on-screen space. CONCLUSIONS: The trans-creation process of cultural and linguistic adaptation led to several design changes that would not have been implemented if we had simply translated the words from English to Spanish.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| 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".