Exploring gender differences in knowledge and practices related to antibiotic use in Southeast Asia: A scoping review
Bibliographic record
Abstract
Inappropriate use of antibiotics has been one of the main contributors to antimicrobial resistance, particularly in Southeast Asia. Different genders are prone to different antibiotic use practices. The objective of this scoping review is to understand the extent and type of evidence available on gender differences in antibiotic use across Southeast Asia. The search strategy for this scoping review involved PubMed, Semantic Scholar, BioMed Central and ProQuest. Two-level screening was applied to identify the final sample of relevant sources. Thematic content analysis was then conducted on the selected final sources to identify recurring themes related to gender differences in antibiotic use and a narrative account was developed based on the themes. Recommendations for next steps regarding reducing inappropriate antibiotic use and gender considerations that need to be made when developing future interventions were also identified. Research on gender and antibiotic use remains scarce. Studies that discuss gender within the context of antibiotic use often mention differences between males and females in knowledge, attitudes and/or behaviour, however, do not explore reasons for these differences. Gender differences in antibiotic use were generally examined in terms of: (i) knowledge of antibiotic use and antimicrobial resistance and (ii) practices related to antibiotic use. Evidence indicated that differences between males and females in knowledge and practices of antibiotic use varied greatly based on setting. This indicates that gender differences in antibiotic use are greatly contextual and intersect with other sociodemographic factors, particularly education and socioeconomic status. Educational interventions that are targeted to meet the specific needs of males and females and delivered through pharmacists and healthcare professionals were the most common recommendations for reducing inappropriate use of antibiotics in the community. Such targeted interventions require further qualitative research on factors influencing differences in knowledge and practices related to antibiotic use among males and females. In addition, there is also a need to strengthen monitoring and regulation practices to ensure accessibility to affordable, quality antibiotics through trusted sources.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".