Understanding malnutrition management through a socioecological lens: Evaluation of a community‐based child malnutrition program in rural Uganda
Bibliographic record
Abstract
In Uganda, almost half of children under 5 years old suffer from undernutrition. Undernutrition, a common form of malnutrition in children, encompasses stunting, wasting and underweight. The causes of child undernutrition are complex, suggesting that interventions to tackle malnutrition must be multifaceted. Furthermore, limited access to healthcare for vulnerable populations restricts the potential of hospital-based strategies. Community-based management of acute malnutrition (CMAM), which includes nutritional counselling, ready-to-use therapeutic foods and the outpatient management of malnutrition by caregivers, is recognised as an effective approach for children's recovery. However, evaluations of CMAM programs are largely based on biomedical and behavioural health models, failing to incorporate structural factors that influence malnutrition management. The objective of this evaluation was to understand the factors influencing malnutrition management in a CMAM program in rural Uganda, using the socioecological model to assess the multilevel determinants of outpatient malnutrition management. This evaluation used qualitative methods to identify factors related to caregivers, healthcare providers and societal structures that influence children's outpatient care. Data were collected at a community health clinic in 2019 through observations and interviews with caregivers of malnourished children. We observed 14 caregiver-provider encounters and interviewed 15 caregivers to examine factors hindering outpatient malnutrition management. Data were thematically analysed informed by the socioecological model. Findings showed that caregivers had a limited understanding of malnutrition. Counselling offered to caregivers was inconsistent and insufficient. Poverty and gender inequality limited caregivers' access to healthcare and their ability to care for their children. Factors at the caregiver and healthcare levels interacted with societal factors to shape malnutrition management. Results suggest that CMAM programs would benefit from providing holistic interventions to tackle the structural barriers to children's care. Using a socioecological approach to program evaluation could help move beyond individual determinants to address the social dynamics shaping malnutrition management in low- and middle-income countries.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| 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".