Iterative Adaptation of a Maternal Nutrition Videos mHealth Intervention Across Countries Using Human-Centered Design: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) video interventions are often transferred across settings. Although the outcomes of these transferred interventions are frequently published, the process of adapting such videos is less described, particularly within and across lower-income contexts. This study fills a gap in the literature by outlining experiences and priorities adapting a suite of South African maternal nutrition videos to the context of rural Burkina Faso. OBJECTIVE: The objective of this study was to determine the key components in adapting a suite of maternal nutrition mHealth videos across settings. METHODS: Guided by the principles of human-centered design, this qualitative study included 10 focus group discussions, 30 in-depth interviews, and 30 observations. We first used focus group discussions to capture insights on local nutrition and impressions of the original (South African) videos. After making rapid adjustments based on these focus group discussions, we used additional methods (focus group discussions, in-depth interviews, and observations) to identify challenges, essential video refinements, and preferences in terms of content delivery. All data were collected in French or Dioula, recorded, transcribed, and translated as necessary into French before being thematically coded by two authors. RESULTS: We propose a 3-pronged Video Adaptation Framework that places the aim of video adaptation at the center of a triangle framed by end recipients, health workers, and the environment. End recipients (here, pregnant or lactating mothers) directed us to (1) align the appearance, priorities, and practices of the video's protagonist to those of Burkinabe women; (2) be mindful of local realities whether economic, health-related, or educational; and (3) identify and routinely reiterate key points throughout videos and via reminder cards. Health workers (here, Community Health Workers and Mentor Mothers delivering the videos) guided us to (1) improve technology training, (2) simplify language and images, and (3) increase the frequency of their engagements with end recipients. In terms of the environment, respondents guided us to localize climate, vegetation, diction, and how foods are depicted. CONCLUSIONS: Design research provided valuable insights in terms of developing a framework for video adaptation across settings, which other interventionists and scholars can use to guide adaptations of similar interventions.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".