Impact of household food insecurity on the use of maternal health services in the Savanes region, Togo: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Food insecurity is a major public health challenge in many parts of the world, especially in sub-Saharan Africa. It affects the health and well-being of vulnerable populations, particularly women of reproductive age in their use of maternal health services. This study explores the impact of food insecurity on the use of maternal health services among Togolese women in the Savanes region, aged between 18 and 49 years. METHODS: This qualitative study was carried out using both focus group discussions (FGD) and in-depth interviews (IDI), which were conducted from March 14th to May 20th, 2022 in three different rural areas of the Savanes region in Togo. Firstly, we conducted twelve in-depth interviews with health professionals in three community health centers. In addition, we conducted three FGDs with 8 participants each in three different rural areas. For analysis, all the data collected were transcribed verbatim, and themes were coded using Nvivo14. RESULTS: Household food insecurity is perceived as a significant threat and barrier to maternal healthcare utilization. Women experiencing food insecurity are less likely to seek maternal health services, as their limited financial resources are prioritized for food rather than healthcare. In contexts of poverty where finances are already precarious, food insecurity further diverts funds that could otherwise be used for medical care. As a result food and financial insecurity intersect influencing women's decisions on whether to access maternal health services. Additionally, the majority of participants identified the COVID-19 pandemic as a factor that seriously exacerbated household food insecurity and consequently further reduced access to maternal healthcare in the region. Enhancing women's socio-economic empowerment and promoting food self-sufficiency were highlighted as potential solutions to improve access to maternal healthcare services while ensuring food security. CONCLUSIONS: Findings from this study highlight the link between food insecurity and maternal healthcare utilization, emphasizing the need to address food insecurity at its root in programs aiming to improve maternal health. Prioritizing poverty reduction through education, income, women's socioeconomic empowerment and food self-sufficiency is crucial. Hence, intersectorial interventions including prenatal nutrition programs are essential to improving access to maternal healthcare.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".