Knowledge, attitudes, motivations and expectations regarding antimicrobial use among community members seeking care at the primary healthcare level: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Inappropriate antibiotic use in (primary healthcare, PHC) settings fuels antimicrobial resistance (AMR), threatens patient safety and burdens healthcare systems. Patients' knowledge, attitudes, motivations and expectations play a crucial role in antibiotic use behaviour, especially in low-income and middle-income countries including South Africa. There is a need to ensure measures of antibiotic use, interventions and future guidance reflect cultural, community and demographic issues associated with patient views to reduce inappropriate use of antibiotics and associated AMR. The objective of this scoping review is to identify key themes surrounding knowledge, attitudes, motivations and expectations among patients and community members regarding antimicrobial use in PHC settings especially in low-income and middle-income countries. METHODS AND ANALYSIS: This scoping review employs a comprehensive search strategy across multiple electronic databases, including OVID, Medline, PubMed and CINHAL, to identify studies addressing patients or community members seeking care at PHC facilities and exploring key drivers of antimicrobial use. The Covidence web-based platform will be used for literature screening and data extraction and the Critical Appraisal Skills Programme qualitative checklist will assess the quality of qualitative papers. Anticipated results will provide an overview of the current evidence base, enabling identification of knowledge gaps. A narrative synthesis of findings will summarise key themes and patterns in patients' knowledge, attitudes, motivations and expectations related to antibiotic use across studies while considering methodological diversity and limitations. ETHICS AND DISSEMINATION: Ethics approval is not required for this scoping review. The findings of this scoping review will be disseminated through publication in a peer-reviewed journal, presentation at relevant conferences and workshops, and collaboration with policy-makers and healthcare stakeholders.
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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 | 0.087 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".