Gender and antimicrobial resistance: a conceptual framework for researchers working in livestock systems
Bibliographic record
Abstract
Background: To promote gender mainstreaming in future AMR research projects and policy implementation within livestock and other systems, researchers need to embrace gender-responsive research methodologies. Ignoring gender considerations can lead to unsustainable interventions and exacerbate existing equity gaps. Incorporating gender analysis is crucial for identifying data collection needs and opportunities to develop gender-responsive research programs and policies. Objectives: We have developed a conceptual framework and a set of research questions designed to enhance the gender-responsiveness of AMR research in livestock systems. Methods: A narrative review previously identified three key entry points for gender dynamic impacting AMR in agricultural systems: gendered antimicrobial resistance exposure, gendered antimicrobial use and gendered outcomes of antimicrobial resistance infections. This information was then analyzed using a health system gender framework. Combining these insights, we developed a comprehensive list of research questions. Results: We developed comprehensive list of gender-related questions. Given the limited understanding of how gender dynamics and norms influence AMR, we have primarily proposed qualitative, exploratory questions. These questions are categorized into two types: integrated and strategic. Integrated questions offer a deeper understanding of gender dynamics and norms in livestock systems with the aim of improving them, while strategic questions focus on gender-related issues in livestock as entry points, highlighting some of the mechanisms behind these gender issues to progress towards gender equality. Conclusion: As gender-analysis in livestock research gains prominence, there is an increasing expectation for AMR researchers to integrate gender considerations into their work. This framework provides a starting point for researchers aiming to enhance gender inclusivity and considerations in AMR research within livestock systems. The next phase of our project will involve applying this framework in the field, where a real-life application will enable its validation and further refinement.
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 | 0.113 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.015 | 0.089 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".