Knowledge, attitudes and perceptions of healthcare workers on antimicrobial stewardship and antibiotic use: a nationwide multi-center study in Bangladesh
Bibliographic record
Abstract
Antimicrobial resistance (AMR) disproportionately affects low and middle income countries (LMICs) including Bangladesh, driven by poorly implemented infection prevention and control (IPC) and misuse of antimicrobials. Robust Antimicrobial Stewardship (AMS) programs mitigate this risk by optimizing prescribing to best-practice standards. This study aimed to assess healthcare workers’ (HCWs) knowledge, attitude and perception (KAP) toward AMS. An online questionnaire was shared via IPC Alliance BD to different health-related institutes and hospitals, inviting voluntary and anonymous participation of HCWs on KAP between January to March 2025. A total of 721 participants from 171 hospitals and institutes (78 public and 93 private), comprising 29% medical officers or equivalent, 29% nurses, 28% professors or equivalent and 14% administrative staff and other roles were enrolled. The average age was 36 years, and females accounted for 54% of respondents. Overall, 43% of respondents were familiar with the term AMS, reached through various sources. Only 11% received AMS training, and 18% had access to standard treatment guidelines. Despite 66% reporting availability of culture and sensitivity (C/S) testing, only 26% routinely recommended C/S and 15% shared an antibiogram. Empirical therapy predominated (> 94%), with a 60% preference for broad-spectrum agents. However, 67% believed rational use and proper IPC can reduce AMR. Knowledge, attitudes, and perceptions varied significantly by age, gender, and education (P < 0.05). Antimicrobial resistance in Bangladesh necessitates strengthened laboratory capacity, expanded public awareness, and improved healthcare worker’s KAP via structured training and continuous monitoring, with robust antimicrobial stewardship as the operational benchmark.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".