Who Has Access to Livestock Vaccines? Using the Social-Ecological Model and Intersectionality Frameworks to Identify the Social Barriers to Peste des Petits Ruminants Vaccines in Karamoja, Uganda
Bibliographic record
Abstract
Access to veterinary services is important in Karamoja, northeastern part of Uganda, as livestock is a primary source of livelihood. Gender is often overlooked in animal health programs, let alone intersectionality. However, given the socio-cultural intricacies of Karamoja, ignoring these factors may hinder animal vaccination practices, limiting the success of programs designed to control and prevent animal diseases, such as peste des petits ruminants (PPR). The study used qualitative research methods, including focus group discussions, individual interviews, and key informant interviews in a participatory research approach to investigate the constraints faced by livestock keepers when accessing vaccines. The study was carried out in Abim, Amudat, Kotido, and Moroto, four districts in the Karamoja Subregion of Uganda. A modified version of the socio-ecological model (SEM) blended with an intersectional approach were used as frameworks to analyze underlying individual, social and structural determinants of vaccine access with intersecting factors of social inequalities. The results show there are seven intersecting factors that influence access to vaccination the most. These are: gender, ethnicity, geographic location, age, physical ability, marital status, and access to education. The impact of these intersections across the different levels of the SEM highlight that there are vast inequalities within the current system. Access to vaccines and information about animal health was most limited among women, widows, the elderly, the disabled, geographically isolated, and those with unfavorable knowledge, attitudes, and practices about vaccination. Cultural norms of communities were also important factors determining access to PPR vaccines. Norms that burden women with household chores and beliefs that women cannot manage livestock, combined with gender-based violence, leaves them unable to participate in and benefit from the livestock vaccine value chain. Trainings and sensitization on gendered intersectional approaches for those involved in the distribution and delivery of vaccines are necessary to avoid exacerbating existing inequalities in Karamoja.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| 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".