Adolescent girls and young women (AGYW) living with HIV in Ghana face multiple intersecting forms of marginalisation. Beyond the clinical management of HIV, little is known about how they construct meaning, navigate identity and imagine their futures within structural contexts shaped by stigma, gender inequity, economic precarity and colonial legacies.

To explore how AGYW living with HIV in Ghana understand and negotiate their social identities in work and school. We then aimed to understand how their lived experiences at school and work are shaped by broader systems of power.

This qualitative study drew on semi-structured interviews with AGYW (ages 11–24, n=24) receiving HIV care in Kumasi, Ghana. Data were coded both inductively and deductively. Themes were interpreted through the Ghanaian context using intersectionality, Critical Disability Studies, spoiled identity theory and African feminist decolonial theory. The analysis was conducted iteratively and reflexively, with attention to positionality, gender and structural power dynamics.

Seven major themes were identified: (1) social support; (2) concrete plans for the future; (3) unattainability of the future; (4) coping via detachment; (5) need for privacy and confidentiality; (6) role as an arbiter of HIV information; and (7) financial stress. Across these themes, AGYW described dynamic processes of identity negotiation, moral and emotional labour and structural constraint. HIV was rarely the sole barrier. Rather, it intersected with gender norms, family dynamics, age hierarchies, economic marginalisation and misinformation to shape participants’ social worlds. Some participants coped through detachment or concealment, while others reclaimed agency through caregiving roles, education or aspirational goals.

AGYW living with HIV in Ghana are not only navigating a chronic illness but also resisting a layered matrix of social and structural injustice. Their stories reveal both vulnerability and strategic agency. Interventions and policy must go beyond biomedical care to address stigma, provide confidential and affirming school and work environments, and offer structural supports for emotional, educational and economic well-being.

Early childhood development (ECD) lays the foundation for lifelong health, academic success and social well-being, yet over 250 million children in low- and middle-income countries are at risk of not reaching their developmental potential. Traditional measures fail to fully capture the risks associated with a child’s development outcomes. Artificial intelligence techniques, particularly machine learning (ML), offer an innovative approach by analysing complex datasets to detect subtle developmental patterns.

To map the existing literature on the use of ML in ECD research, including its geographical distribution, to identify research gaps and inform future directions. The review focuses on applied ML techniques, data types, feature sets, outcomes, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and reported limitations.

Scoping review using the Arksey and O‘Malley framework with enhancements by Levac et al.

A systematic search was conducted on 16 June 2024 across PubMed, Web of Science, IEEE Xplore and PsycINFO, supplemented by grey literature (OpenGrey) and reference hand-searching. No publication date limits were applied.

Included studies applied ML or its variants (eg, deep learning (DL), natural language processing) to developmental outcomes in children aged 0–8 years. Studies were in English and addressed cognitive, language, motor or social-emotional development. Excluded were studies focusing on robotics; neurodevelopmental disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder and communication disorders; disease or medical conditions; and review articles.

Three reviewers independently extracted data using a structured MS Excel template, covering study ML techniques, data types, feature sets, outcomes, outcome measures, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and limitations. A narrative synthesis was conducted, supported by descriptive statistics and visualisations.

Of the 759 articles retrieved, 27 met the inclusion criteria. Most studies (78%) originated from high-income countries, with none from sub-Saharan Africa. Supervised ML classifiers (40.7%) and DL techniques (22.2%) were the most used approaches. Cognitive development was the most frequently targeted outcome (33.3%), often measured using the Bayley Scales of Infant and Toddler Development-III (33.3%). Data types varied, with image, video and sensor-based data being most prevalent. Key predictive features were grouped into six categories: brain features; anthropometric and clinical/biological markers; socio-demographic and environmental factors; medical history and nutritional indicators; linguistic and expressive features; and motor indicators. Most studies (74.1%) focused solely on prediction, with the majority conducting predictions at age 2 years and above. Only 41% of studies employed explainability methods, and validation strategies varied widely. Few studies (7.4%) conducted external validation, and only one had progressed to a clinical trial. Common limitations included small sample sizes, lack of external validation and imbalanced datasets.

There is growing interest in using ML for ECD research, but current research lacks geographical diversity, external validation, explainability and practical implementation. Future work should focus on developing inclusive, interpretable and externally validated models that are integrated into real-world implementation.