Addressing anaemia in pregnancy in rural plains Nepal: A qualitative, formative study
Bibliographic record
Abstract
Maternal anaemia prevalence in low-income countries is unacceptably high. Our research explored the individual-, family- and community-level factors affecting antenatal care uptake, iron folic acid (IFA) intake and consumption of micronutrient-rich diets among pregnant women in the plains of Nepal. We discuss how these findings informed the development of a home visit and community mobilisation intervention to reduce anaemia in pregnancy. We used a qualitative methodology informed by the socio-ecological framework, conducting semi-structured interviews with recently pregnant women and key informants, and focus group discussions with mothers-in-law and fathers. We found that harmful gender norms restricted women's access to nutrient-rich food, restricted their mobility and access to antenatal care. These norms also restricted fathers' role to that of the provider, as opposed to the caregiver. Pregnant women, mothers-in-law and fathers lacked awareness about iron-rich foods and how to manage the side effects of IFA. Fathers lacked trust in government health facilities affecting access to care and trust in the efficacy of IFA. Our research informed interventions by (1) informing the development of intervention tools and training; (2) informing the intervention focus to engaging mothers-in-law and men to enable behaviour change; and (3) demonstrating the need to work in synergy across individual, family and community levels to address power and positionality, gender norms, trust in health services and harmful norms. Participatory groups and home visits will enable the development and implementation of feasible and acceptable strategies to address family and contextual issues generating knowledge and an enabling environment for behaviour change.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".