COVID-19, nutrition, and gender: An evidence-informed approach to gender-responsive policies and programs
Bibliographic record
Abstract
In addition to the direct health impacts of COVID-19, government and household mitigation measures have triggered negative indirect economic, educational, and food and health system impacts, hitting low-and middle-income countries the hardest and disproportionately affecting women and girls. We conducted a gender focused analysis on five critical and interwoven crises that have emerged because of the COVID-19 crisis and exacerbated malnutrition and food insecurity. These include restricted mobility and isolation; reduced income; food insecurity; reduced access to essential health and nutrition services; and school closures. Our approach included a theoretical gender analysis, targeted review of the literature, and a visual mapping of evidence-informed impact pathways. As data was identified to support the visualization of pathways, additions were made to codify the complex interrelations between the COVID-19 related crises and underlying gender relations. Our analysis and resultant evidence map illustrate how underlying inequitable norms such as gendered unprotected jobs, reduced access to economic resources, decreased decision-making power, and unequal gendered division of labor, were exacerbated by the pandemic's secondary containment efforts. Health and nutrition policies and interventions targeted to women and children fail to recognize and account for understanding and documentation of underlying gender norms, roles, and relations which may deter successful outcomes. Analyzing the indirect effects of COVID-19 on women and girls offers a useful illustration of how underlying gender inequities can exacerbate health and nutrition outcomes in a crisis. This evidence-informed approach can be used to identify and advocate for more comprehensive upstream policies and programs that address underlying gender inequities.
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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 | 0.106 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".