Learning from over ten years of implementing the One Health approach in the Democratic Republic of Congo: A qualitative study
Bibliographic record
Abstract
The Democratic Republic of Congo (DRC) has faced emerging infectious diseases such as Ebola, Mpox and Yellow fever, and antimicrobial resistance is a growing concern. To address these issues, in 2011 the country embarked on implementing the One Health (OH) approach at the national and provincial levels. This study investigates OH institutionalization and implementation in the DRC, describes the process of OH decentralization, and identifies the opportunities and challenges of sustaining these efforts. We conducted a qualitative study based on literature, document review and key informant interviews. The literature search targeted PubMed, Google Scholar and the document depository of the national One Health platform (NOHP). Key informant identified at the national level included ministry representatives, OH platform members and donors supporting OH implementation. These interviews were conducted in-person and online, recorded, transcribed, and imported into Dedoose software (version 9.2.006) for coding. Content analysis was performed to identify activities, processes, and achievements during the implementation of OH in DRC. Results of the literature and document review ( n = 72) and analysis of stakeholder interviews ( n = 24) indicate that a national OH platform, initiated in 2011, is hosted at the Ministry of Higher Education and coordinates other sectors. It comprises governmental departments, academic institutions, and civil society organizations working at the human, animal, and environment sectors. The governance structure includes a national coordinator, a permanent secretariat, technical working groups, and subnational entities at provincial and territorial levels. Following the establishment of the national OH platform, a structured process foresees to facilitate OH implementation at the provincial and territorial levels. Achievements up to today include the development of training programs, establishment of OH committees in some provinces, assessments of workforce needs, formulation of a national strategy, development of governance manuals, and support to the Mpox response coordination. Nevertheless, OH implementation in the DRC faces challenges, including leadership tensions at the national level, inadequate domestic funding, limited training and capacity building for professionals, and insufficient infrastructure for data collection and sharing. Strengthening leadership and coordination, advocating for domestic resource mobilization, and strengthening infrastructure for data collection and sharing while ensuring equity across sectors is essential for advancing the OH agenda and ensuring its efficacy. • One Health, an integrated approach, is advocated to strengthen health systems and disease prevention and management at the national and subnational levels. • We present lessons learnt from One Health implementation in the Democratic Republic of Congo, which provide insights on best practice, challenges, and gaps. • Sustainability will be achieved by mobilizing more domestic resources and training professionals. Mid-term, intersectoral tensions must be addressed, and infrastructures for data collection and sharing should be established to achieve the expected impact.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".