The COVID-19 pandemic and self-reported food insecurity among women in Burkina Faso: evidence from the performance monitoring for action (PMA) COVID-19 survey data
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic led to widespread economic disruptions, with government-imposed restrictions and lockdowns significantly affecting livelihoods globally. Burkina Faso, a country with pre-existing vulnerabilities in food security, experienced considerable challenges during this period. The aim of this study was to examine how COVID-19-related income losses is associated with self-reported food insecurity among women in Burkina Faso in 2020. The study also examined whether there was an increase in self-reported food insecurity among women during the COVID-19 restrictions compared with the pre-pandemic era. METHODS: We conducted a cross-sectional analysis using data from the Performance Monitoring for Action (PMA) female survey, which included 3,499 women from Burkina Faso. This study examined the associations between socioeconomic variables, such as age, education, household income loss, and food insecurity. We conducted two analyses using logistic regression. The first analysis focused on self-reported food insecurity and its association with the socioeconomic variables, and the second analysis focused on whether there was an increase in self-reported food insecurity compared with pre-pandemic levels and its association with the socioeconomic factors. We controlled for relevant confounders in the analysis and presented the results as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). RESULTS: Our findings indicated that 16.97% of women reported experiencing food insecurity during the pandemic period. Compared with women with no income loss, women who experienced partial household income loss were 1.82 times (95% CI: 0.98-3.38) more likely to report food insecurity, whereas those who experienced complete income loss were 5.16 times (95% CI: 2.28-9.43) more likely to report food insecurity. The study, however, did not find a statistically significant increase in self-reported food insecurity due to COVID-19 restrictions compared with pre-pandemic levels. CONCLUSIONS: This study demonstrated that income loss due to COVID-19 restrictions profoundly affected women's food security in Burkina Faso. The significant associations between income loss and increased food insecurity underscore the need for targeted interventions and safety nets to support women during public health crises.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".