Animal health emergencies: a gender-based analysis for planning and policy
Bibliographic record
Abstract
There has been increasing recognition of gender-based inequity as a barrier to successful policy implementation. This consensus, coupled with an increasing frequency of emergencies in human and animal populations, including infectious disease events, has prompted policy makers to re-evaluate gender-sensitivity in emergency management planning. Seeking to identify key publications relating to gendered impacts and considerations across diverse stakeholders in different types of animal health emergencies, we conducted a non-exhaustive, targeted scoping review. We developed a matrix for both academic and policy literature that separated animal health emergencies into two major categories: humanitarian crises and infectious disease events. We then conducted semi-structured interviews with key animal health experts. We found minimal evidence of explicit gender responsive planning in animal health emergencies, whether humanitarian or infectious disease events. This was particularly salient in Global North literature and policy planning documents. Although there are some references to gender in policy documents pertaining to endemic outbreaks of African swine fever (ASF) in Uganda, most research remains gender blind. Despite this, implicit gendered themes emerged from the literature review and interviews as being direct or indirect considerations of some research, policy, and implementation efforts: representation; gendered exposure risks; economic impact; and unpaid care. Absent from both the literature and our conversations with experts were considerations of mental health, gender-based violence, and intersectional impacts. To remedy the gaps in gender-based considerations, we argue that the intentional inclusion of a gender transformative lens in animal health emergency planning is essential. This can be done in the following ways: (1) collection of disaggregated data (race, gender, sexual orientation, etc.); (2) inclusion of gender experts; and (3) inclusion of primary gendered impacts (minimal representation of women in policy positions, gender roles, economic and nutrition impacts) and secondary gendered impacts (gender-based violence, mental health, additional unpaid care responsibilities) in future planning.
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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 | 0.056 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".