Food Insecurity and Child Mental Health in Masaka District, Uganda: Qualitative Study Using a Realist Thematic Analysis
Bibliographic record
Abstract
Abstract Background Food insecurity and child mental health difficulties often intersect in complex ways, yet the mechanisms underpinning these relationships remain underexplored in resource-limited settings. This study aimed to develop a conceptual framework linking food insecurity and mental health difficulties among children in a low-resource Ugandan context. Methods A qualitative design was employed, consisting of 12 focus group discussions with 36 teachers across four schools. Teachers provided accounts of children’s experiences of food insecurity and associated psychosocial difficulties, drawing on sustained observation over 9 months. Data were analysed using realist thematic analysis, with nine coder consensus meetings and member checking with all participants to enhance rigour and interpretive validity. Themes were synthesised into an integrated conceptual framework. Results The framework identified three interlinked pathways: social causation, where food insecurity (e.g., hunger, stigma, irregular meals) precipitated psychosocial distress; social drift, where pre-existing child or caregiver mental health difficulties disrupted family functioning and food provision; and bidirectional pathways, where these processes reinforced one another through recursive feedback loops. Together, the framework shows that food insecurity and child mental health difficulties interact in cyclical, context-dependent ways. Conclusions Findings highlight the cyclical, context-dependent nature of food insecurity and child mental health in resource-limited settings. Interventions are needed that integrate food security, nutrition, psychosocial support, and structural reforms. Schools represent critical entry points; however, broader policy action on poverty alleviation and social protection is also essential. This framework has provided a foundation for future longitudinal and intervention studies, as well as child-centred measurement innovations.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| 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".