The intra-household gendered burden of animal diseases in livestock-producing households in Ethiopia: Results from key informant interviews and a scoping review
Bibliographic record
Abstract
Abstract Ethiopia is a highly agrarian economy, though livestock’s contribution falls below its potential. Women play a significant role in livestock production; however, the literature on gendered dynamics of livestock disease is limited, particularly at the intra-household level. This work marks the first gender-focused study within the Global Burden of Animal Disease programme. Its goal is to enhance the programme’s aim of disaggregating the economic burden borne by humans due to animal disease. It explores the extent to which existing knowledge can be disaggregated by gender within households. A scoping review of the existing literature on the intra-household burden of animal disease in Ethiopia was conducted, with 143 articles screened. This was supplemented by seventeen key informant interviews consisting of individuals or knowledgeable representatives from organisations known to the authors for their work in Ethiopia and/or on gender, livestock production, and animal disease. Only one study directly addressed the intra-household gendered dimensions of animal disease burden in Ethiopia. Data were extracted in MS Excel. Adult men and women were found to be most impacted due to their roles in income generation and providing animal-sourced foods, and their need to compensate for losses during disease outbreaks. However, all household members contribute to disease transmission through gender-specific responsibilities. Key informant interviews were analysed in NVivo to determine themes in responses. Participants noted that household members engage in distinct transmission activities and face unequal consequences shaped by gendered norms and emphasised the need for gender-disaggregated data. We advocate for primary, contextually grounded data collection that routinely includes gender- and age-disaggregated measures of exposure, decision-making, empowerment, and economic outcomes, complemented by qualitative enquiry. This would enable the design of targeted interventions to reduce animal and human morbidity and mortality while protecting livelihoods and promoting equity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".