Gender and equity considerations in AMR research: a systematic scoping review
Bibliographic record
Abstract
Research on gender and antimicrobial resistance (AMR) beyond women's biological susceptibility is limited. A gender and equity lens in AMR research is necessary to promote gender equality and support the effectiveness, uptake, and sustainability of real-world AMR solutions. We argue that it is an ethical and social justice imperative to include gender and related intersectional issues in AMR research and implementation. An intersectional exploration of the interplay between people's diverse identities and experiences, including their gender, socio-economic status, race, disability, age, and sexuality, may help us understand how these factors reinforce AMR risk and vulnerability and ensure that interventions to reduce the risk of AMR do not impact unevenly. This paper reports on the findings of a systematic scoping review on the interlinkages between AMR, gender and other socio-behavioural characteristics to identify priority knowledge gaps in human and animal health in LMICs. The review focused on peer-reviewed and grey literature published between 2017 and 2022. Three overarching themes were gendered division of caregiving roles and responsibilities, gender power relations in decision-making, and interactions between gender norms and health-seeking behaviours. Research that fails to account for gender and its intersections with other lines of disadvantage, such as race, class and ability, risks being irrelevant and will have little impact on the continued and dangerous spread of AMR. We provide recommendations for integrating an intersectional gender lens in AMR research, policy and practice.
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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 | 0.060 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".