Planning with a gender lens: A gender analysis of pandemic preparedness plans from eight countries in Africa
Bibliographic record
Abstract
Background: Health planning and priority setting with a gender lens can help to anticipate and mitigate vulnerabilities that women and girls may experience in health systems, which is especially relevant during health emergencies. This study examined how gender considerations were accounted for in COVID-19 pandemic response planning in a subset of countries in Africa. Methods: Multi-country document review of national pandemic response plans (published before July 2020 and as of March 2022) from Ethiopia, Ghana, Kenya, Nigeria, Rwanda, South Africa, Uganda, and Zambia, supplemented with secondary data on gender representation on planning committees. A gender analysis framework informed the study design and the Morgan et al. matrix guided data extraction and analysis. Results: All plans reflected implicit and explicit considerations of the impacts of the pandemic responses on women and girls. Through a gender lens, the implicit considerations focused on ensuring safety and protections (e.g., training, access to personal protective equipment) for community and facility-based health care workers and broad engagement of the community in risk communication. The explicit gender considerations, reflected in a minority of plans, focused on addressing gender-based violence and providing access to essential services (e.g., sexual and reproductive health care, psychosocial supports), products (e.g., menstrual hygiene products) and social protection measures. Women were underrepresented on the COVID-19 planning committees in all countries. Conclusions: The plans reflected varying national efforts to develop pandemic responses that anticipated and reflected unique vulnerabilities faced by women, though subsequent plans reflected further consideration of gender-relevant impacts compared to initial plans. Embedding a gender lens in emergency preparedness planning furthers equity and could support anticipation and timely mitigation of negative outcomes for women and girls who are often further marginalized during health emergencies.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| 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".