Cultural and Contextual Adaptation of Digital Health Interventions: Narrative Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Emerging evidence suggests that positive impacts can be generated when digital health interventions are designed to be responsive to the cultural and socioeconomic context of their intended audiences. OBJECTIVE: This narrative review aims to synthesize the literature about the cultural adaptation of digital health interventions. It examines how concepts of culture and context feature in design and development processes, including the methods, models, and content of these interventions, with the aim of helping researchers to make informed decisions about how to approach cultural adaptation in digital health. METHODS: Literature searches for this narrative review were conducted across 4 databases. Following full-text article screening by 2 authors, 16 studies of interventions predominantly focused on the self-management of health were selected based on their detailed focus on the process of cultural adaptation. Key considerations for cultural adaptation were identified and synthesized through a qualitative narrative approach, enabling an integrative and in-depth understanding of cultural adaptation. RESULTS: The literature demonstrates varying approaches and levels of cultural adaptation across stages of intervention development, involving considerations such as the research ethos orienting researchers, the methodologies and models used, and the resultant content adaptations. In relation to the latter, culturally appropriate and accessible user interface design and translation can be seen as particularly important in shaping the level of adaptation. CONCLUSIONS: Optimizing cultural adaptation involves linking culture with other contextual factors such as economic conditions and social systems to ensure accessibility and the sustained use of digital health interventions. Culturally humble approaches that use the involvement of a broad range of participants, experts, and other stakeholders are demonstrated to spark vital insights for content development, implementation, and evaluation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it