A qualitative assessment of gender roles in child nutrition in Central Malawi
Bibliographic record
Abstract
BACKGROUND: Child malnutrition persists globally with men and women playing distinct roles to support children's nutrition. Women frequently carry the bulk of the workload related to food, care, and health, all of which are critical factors in child nutrition. For this reason, development efforts have emphasised women ignoring the potential role of men in supporting children's nutrition. This study sought to understand the different roles that Malawian men and women play in children's nutrition. METHODS: This qualitative was conducted in rural Central Malawi as part of a baseline study in 2017 for the CARE Southern Africa Nutrition Initiative. Seventy-six participants were interviewed, including 19 men and 57 women, using focus group discussions and in-depth interviews. We sought to understand the gender distribution of men's and women's roles and how these roles influence child nutrition. RESULTS: We found that both men and women were involved in productive, reproductive, and community work. However, consistent with the literature, women carried a disproportionate workload in supporting child nutrition compared to men. Women's heavier workloads often prevented them from being able to meet children's food needs. Nevertheless, shifts in gender roles were observed in some of the sampled communities, with men taking up responsibilities that have been typically associated with women. These changes in gender roles, however, did not necessarily increase women's power within the household. CONCLUSIONS: Traditional gender roles remain prevalent in the sampled communities. Women continue to be primarily responsible for the food, care, and health of the household. Women's heavy workloads prevent them from providing optimal care and nutrition for children. While efforts to advance gender equality by encouraging men to participate in child care and other household responsibilities appear to have had marginal success, the extent to which these efforts have successfully encouraged men to share power remains unclear. Improving gender equality and child nutrition will require efforts to redistribute gendered work and encourage men to move towards shared power with women over household decision-making and control over income.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".