Barriers and facilitators of implementing interventions to improve appropriate antibiotic use in low- and middle-income countries: a systematic review based on the Consolidated Framework for Implementation Research
Bibliographic record
Abstract
Abstract Background Behavior change interventions that aim to improve rational antibiotic use in prescribers and users have been widely conducted in both high- and LMICs. However, currently, no review has systematically examined challenges unique to LMICs and offered insights into the underlying contextual factors that influence these interventions. We adopted an implementation research perspective to systematically synthesize the implementation barriers and facilitators in LMICs. Methods We conducted literature searches in five electronic databases and identified studies that involved the implementation of behavior change interventions to improve appropriate antibiotic use in prescribers and users in LMICs and reported implementation barriers and facilitators. Behavior change interventions were defined using the behavior change wheel, and the coding and synthesis of barriers and facilitators were guided by the Consolidated Framework for Implementation Research (CFIR). Results We identified 52 eligible studies, with the majority targeting prescribers practicing at tertiary facilities (N=39, 75%). The most commonly reported factors influencing implementation were found in the inner setting domain of the CFIR framework, particularly related to constraints in resources and the infrastructure of the facilities where interventions were implemented. Barriers related to the external policy environment (e.g., lack of national initiatives and policies on antibiotic use), and individual characteristics of target populations (e.g., reluctance to change prescribing behaviors) were also common, as well as facilitators related to intervention characteristics (e.g., embedding interventions in routine practice) and process (e.g., stakeholder engagement). We also provided insights into the interrelationships between these factors and the underlying causes contributing to the implementation challenges in LMICs. Conclusion We presented a comprehensive overview of the barriers and facilitators of implementing behavior change interventions to promote rational antibiotic use in LMICs. Our findings suggest that facilitating the implementation of interventions to improve rational antibiotic use needs comprehensive efforts to address challenges at policy, organizational, and implementation levels. Specific strategies include (1) strengthening political commitment to prompt mobilization of domestic resources and formulation of a sustainable national strategy on AMR, (2) improving the infrastructure of health facilities that allow prescribers to make evidence-based clinical decisions, and (3) engaging local stakeholders to improve their buy-in and facilitate contextualizing interventions. Trial registration PROSPERO: CRD42021252715 .
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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 | 0.069 | 0.186 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".