Putting gender upfront in One Health AMR research and implementation strategies
Bibliographic record
Abstract
Abstract Despite a gendered approach being increasingly applied across global health challenges, this has been a notable oversight in antimicrobial resistance (AMR) research. Failing to consider the complexity of human behaviours and roles in healthcare, animal production, and environmental settings compromises programmatic effectiveness and sustainability, while risking entrenching or widening existing social disparities. Research demonstrates how gender norms influence health-related behaviours across various sectors, from accessing healthcare to livestock rearing, with strong implications for antimicrobial stewardship and zoonotic disease transmission. The sparse literature connecting and investigating gender, equity and AMR – especially in a One Health context – hampers our ability to comprehensively address this global issue. The responsibility is however shared. This commentary advocates for funders to propel the inclusion of gender and equity-focused perspectives in AMR research and to set expectations in research landscapes, thus fostering a more equitable global health landscape and strengthening strategies against AMR. We outline our rationale and recommendations for other funders to support an ecosystem in AMR that supports gender and equity as a common aspect of AMR research and not an exception. One Health impact statement While AMR is increasingly recognised as a One Health challenge encompassing humans, animals, plants and the environment, sector-specific and cross-sectoral solutions needed to address AMR too often lack the multidisciplinary approach needed for a holistic response to this challenge. To date, AMR research has been largely biomedical, with limited social investigation including on gender and its interplay with factors that drive AMR in different settings such as healthcare, community or farm settings. Intentionally integrating gender analysis that informs AMR research design and implementation across sectors, including with supportive research funding opportunities, will help build the evidence base on how research projects and public programs should integrate and address gender disparities in AMR across the One Health spectrum. This is needed to ensure contextually relevant gender-informed solutions with sustainable impact.
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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 | 0.313 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.008 | 0.047 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".