Food Insecurity and Child Mental Health in Masaka District, Uganda: Qualitative Study Using a Realist Thematic Analysis
Bibliographic record
Abstract
Abstract Food insecurity and child mental health difficulties frequently intersect, yet the mechanisms and pathways connecting them remain underexplored in resource-limited settings. This study developed a contextually grounded explanatory model to examine these relationships within a single rural Ugandan setting. We conducted 12 focus group discussions with 36 teachers across four schools in Masaka district, drawing on their sustained observations of children’s food insecurity and mental health difficulties across a nine-month timeframe. Data were analysed using realist thematic analysis, supported by iterative coding, nine consensus meetings and member checking. Three interrelated pathways were identified. In the social causation pathway, food insecurity, manifested through hunger-related stress, food-related stigma and irregular or inadequate meals, preceded and contributed to mental health difficulties. In the social drift pathway, preexisting mental health difficulties among caregivers and/or children disrupted household functioning and food provision, increasing vulnerability to food insecurity. A bidirectional pathway captured recursive processes in which food insecurity and mental health difficulties co-evolved and reinforced one another over time. These pathways shaped children’s mental health and educational engagement within school environments. The findings offer context-specific insights that may inform research in similar settings. They also highlight the potential value of integrated, multi-level interventions, while underscoring the need for longitudinal and intervention research.
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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".