Women’s Empowerment and Livestock Vaccination: Evidence from Peste des Petits Ruminants Vaccination Interventions in Northern Ghana
Bibliographic record
Abstract
Healthy livestock provide meaningful opportunities to enhance women's empowerment (WE) in low- and middle-income countries. Animal vaccines are important to keep livestock healthy and productive. However, gender-based restrictions limit women's access to animal health services, thereby affecting the potential of livestock to enhance their empowerment. While growing empirical evidence reveals that women-controlled livestock (e.g., small ruminants) have important implications for WE and support better household nutrition outcomes, little empirical evidence exists from rigorous analyses of the relationship between WE and animal vaccines for women-controlled livestock species. Our analysis explores the relationship between WE and involvement with PPR vaccination in Ghana. Data collected using the Women's Empowerment in Livestock Index (WELI) tool from 465 women and 92 men farmers (who keep goats) from northern Ghana, and analyzed using PLS-SEM, revealed a significant direct positive association between knowledge about animal health and PPR vaccines and a significant indirect positive association between access to PPR vaccines and empowerment. The empowerment of women goat farmers, as revealed by our model's results for the relationship between empowerment and vaccine facets, was significantly represented by asset ownership and input into decisions concerning livestock. These study results reveal important considerations in designing effective and equitable livestock vaccine systems.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".