Patient knowledge, attitudes and behaviors related to antimicrobial use in South African primary healthcare settings: development and testing of the CAMUS and its implications
Bibliographic record
Abstract
Background Antimicrobial resistance (AMR) poses a global health threat, particularly in low- and middle-income countries (LMICs) including South Africa where limited resources and knowledge gaps exacerbate inappropriate antimicrobial use. To address this, the community antimicrobial use scale (CAMUS) was developed to assess patients’ knowledge, attitudes and behaviors regarding antimicrobial use in South African primary healthcare (PHC) settings, with the aim of informing antimicrobial stewardship (AMS) strategies. Methods Development of the CAMUS was informed by a scoping review and theoretical constructs from the Health Belief Model, Social Cognitive Theory, and Theory of Planned Behavior. A pilot study was subsequently conducted in two South African districts, an urban and a rural district, with 30 adult participants to provide insights into patients’ understanding of the items. Data collection involved administering CAMUS alongside a health literacy test followed by cognitive interviews to refine clarity and ensure understanding. A feasibility assessment was also conducted to evaluate the practical use of CAMUS in PHC settings. Results Participants demonstrated varied knowledge of antimicrobial use. While 60% correctly identified antibiotics as effective for bacterial infections, 93.33% incorrectly believed antibiotics could treat viral illnesses such as colds. Marginal health literacy was prevalent (86.67%). The CAMUS demonstrated feasibility, with an average completion time of 10 minutes. Questions were iteratively revised to improve future clarity and relevance based on the results of the cognitive interviews. Key findings highlighted misconceptions about antibiotics and the influence of social norms and systemic barriers on antimicrobial use behaviors. Conclusion The CAMUS effectively captures the knowledge, attitudes and behaviors of antimicrobial use in South African PHC settings. Pilot testing demonstrated its feasibility to use it as a tool to assess patient knowledge, attitudes and behaviors related to antimicrobial use in a larger population, to subsequently guide AMS initiatives by addressing knowledge gaps and related barriers to improve future antimicrobial use. Future research will include development of a shorter version of the CAMUS, followed by validation in larger, more diverse populations and in local languages to enhance its usability when investigating antimicrobial use and AMR across LMICs.
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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".