Global knowledge, attitudes, and practices towards antimicrobial resistance among healthcare workers: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The rising prevalence of antimicrobial resistance (AMR) poses a critical global health challenge. Healthcare workers (HCWs) play a pivotal role in combating AMR by implementing effective preventive strategies and adhering to good practices. This study aimed to evaluate the global knowledge, attitudes, and practices (KAP) of HCWs towards AMR. METHODS: A comprehensive search of PubMed/MEDLINE, ScienceDirect, Scopus, Web of Science, Cochrane Library, and Google Scholar was conducted for English-language articles published up to August 2024. Inclusion criteria were observational studies reporting KAP data among HCWs related to AMR. Study quality was assessed using the Joanna Briggs Institute critical appraisal checklist. Statistical analyses, including heterogeneity (I² statistic, Cochran Q), were conducted using STATA version 14. Random-effects models were applied for pooled estimates, and subgroup analyses, meta-regression, and sensitivity analyses were performed. Publication bias was assessed via Egger's test and adjusted using the trim-and-fill method. Geographical distribution was analyzed with ArcGIS 10.3 software, and evidence certainty was evaluated using the GRADE framework. RESULTS: A meta-analysis of 108 studies involving 29,433 HCWs assessed their knowledge of AMR. Additionally, 51 studies with 13,660 HCWs evaluated attitudes, and 43 studies with 10,569 HCWs examined practices regarding AMR. The pooled proportion of HCWs with good knowledge of AMR was 56.5% (95% CI: 50.4-62.6%, I² = 99.5%), with the highest prevalence in Europe (70.3%) and the lowest in the Western Pacific (45.9%). Positive attitudes towards AMR were reported in 60.4% (95% CI: 48.5-72.3%, I² = 99.8%), with the highest prevalence in the Eastern Mediterranean Region (64.5%) and among those with less than five years of experience (77.8%). Good practices were observed in 48.5% (95% CI: 36.5-60.5%, I² = 99.7%), with the highest adherence in Europe (56.6%) and the lowest in Africa (39.1%). Subgroup analysis revealed that younger HCWs (under 30 years) showed better KAP scores across all domains. CONCLUSION: The findings underscore the need for targeted interventions to enhance the knowledge, attitudes, and practices of HCWs regarding AMR. Priority should be given to designing and implementing robust training programs tailored to the specific needs of HCWs in resource-constrained settings. Strengthening AMR-related education and practice among HCWs is crucial for combating the global AMR crisis effectively.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".