FROM BRAZIL TO CANADA: TRANSLATION AND CROSS-CULTURAL ADAPTATION OF AN APP FOR BREASTFEEDING PREMATURE BABIES
Bibliographic record
Abstract
ABSTRACT Objective: Translating and culturally adapting the AmamentaCoach app, originally developed in Brazil, for use by mothers of premature babies in Canada, through international research collaboration. Method: This is applied research in the form of technological development, in which the World Health Organization's recommendations for the translation and cultural adaptation of instruments were taken into account. Five Canadian researchers who are experts in breastfeeding promotion evaluated the appearance, language, and content of the new version of the app. An 18-item instrument was used with Likert scale response options indicating the degree of agreement for each statement, where 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree, and 5=Strongly Agree. Results: In the first round of evaluation, the total Content Validity Index (CVI-T) was 0.72, and 6 of the 18 items did not reach CVI-I ≥0.8. A total of 59 screens (74%) of the app were modified, especially in terms of language and the quality of the translation of the texts, reaching CVI-T=0.87 in the second round. Conclusion: The Breastfeeding Coach app showed expressions adjusted to Canadian reality, meaningful images, and new auxiliary resources specific to Canada. Changes in content and attenuations in the rhetorical textual pattern, prompted by cultural differences in the nurse-client relationship and the woman's role in these two different societies, sought consistency with the reality of breastfeeding practices in Canada.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".