Supporting One Health policies to manage antibiotic resistance in Senegal: a systems analysis using group model building
Bibliographic record
Abstract
Introduction: Antibiotic resistance (ABR) is a growing public health issue in Senegal, driven by interconnected factors across human, animal, and ecosystem health. This study applied a participatory systems approach to map the factors influencing ABR in Senegal and identify possible policy actions from a One Health perspective. Methods: A group model building workshop was held in October 2023 in Dakar with 22 stakeholders from diverse professions and sectors, including human and animal health, environment, agriculture, and food safety. Causal loop diagrams were co-developed to map factors driving ABR and identify intervention points. Results: The 22 participants identified 55 factors and 88 connections between those factors, that together contribute to the emergence and spread of ABR in Senegal. Four feedback loops were identified: (1) demand for antibiotics; (2) misinformation, public perception and alternative treatments; (3) development of context-appropriate regulations; and (4) enforcement of regulations. Participants proposed 36 actions for ABR mitigation, focusing on: laboratory capacity development; healthcare and infection prevention and control; rational use of antimicrobials in human and animal health; and coordination, communication, and research. Actions considered to have the greatest potential to positively transform the system included: investment in laboratory capacity; enforcement of regulations against the illegal sale of medications; and harmonization of data collection procedures across surveillance systems. Discussion: This study highlights the value of participatory systems approaches for mapping key drivers of ABR and identifying potential ABR policy actions. While this work integrates cross-sectoral perspectives and provides some actionable insights for evidence-informed decision making, the findings reflect the perspectives of national-level actors and shows strong alignment with international policy and priorities. ABR policy design should also involve local authorities and populations to ensure effective and context-appropriate action. This study provides new empirical evidence to support the development of ABR policy in Sub-Saharan Africa by highlighting the interrelationships between policy areas and the knock-on effects that sectoral and cross-sectoral interventions can have.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".