Developing Culturally Appropriate Content for a Child-Rearing App to Support Young Children’s Socioemotional and Cognitive Development in Afghanistan: Co-Design Study
Bibliographic record
Abstract
BACKGROUND: Optimal child-rearing practices can help mitigate the consequences of detrimental social determinants of health in early childhood. Given the ubiquity of personal digital technologies worldwide, the direct delivery of evidence-based information about early childhood development holds great promise. However, to make the content of these novel systems effective, it is crucial to incorporate place-based cultural beliefs, traditions, circumstances, and value systems of end users. OBJECTIVE: This paper describes the iterative approach used to develop the Thrive by Five child-rearing app in collaboration with Afghan parents, caregivers (eg, grandparents, aunts, and nannies), and subject matter experts (SMEs). We outline how co-design methodologies informed the development and cultural contextualization of content to meet the specific needs of Afghan parents and the content was tested and refined in collaboration with key Afghan stakeholders. METHODS: The preliminary content was developed based on a comprehensive literature review of the historical and sociocultural contexts in Afghanistan, including factors that influence child-rearing practices and early childhood development. After an initial review and refinement based on feedback from SMEs, this content was populated into a beta app for testing. Overall, 8 co-design workshops were conducted in July and August 2021 and February 2022 with 39 Afghan parents and caregivers and 6 SMEs to collect their feedback on the app and its content. The workshops were audio recorded and transcribed; detailed field notes were taken by 2 scribes. A theoretical thematic analysis using semantic codes was conducted to inform the refinement of existing content and development of new content to fulfill the needs identified by participants. RESULTS: The following 4 primary themes were identified: child-rearing in the Afghan sociocultural context, safety concerns, emotion and behavior management, and physical health and nutrition. Overall, participants agreed that the app had the potential to deliver valuable information to Afghan parents; however, owing to the volatility in the country, participants recommended including more activities that could be safely done indoors, as mothers and children are required to spend most of their time at home. Additionally, restrictions on public engagement in music required the removal of activities referencing singing that might be performed outside the home. Further, activities to help parents reduce their children's screen time, promote empathy, manage emotions, regulate behavior, and improve physical health and nutrition were requested. CONCLUSIONS: Direct engagement with Afghan parents, caregivers, and SMEs through co-design workshops enabled the development and refinement of evidence-based, localized, and contextually relevant child-rearing activities promoting healthy social, emotional, and cognitive development during the first 5 years of children's lives. Importantly, the content was adapted for the ongoing conflict in Afghanistan with the aim of empowering Afghan parents and caregivers to support their children's developmental potential despite the security concerns and situational stressors.
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.
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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".