Biomedical waste management in Dakar, Senegal: legal framework, health and environment issues; policy and program options
Bibliographic record
Abstract
Increases in population and the number of health-care facilities in Dakar has led to considerable increase in biomedical waste (BMW) generation, posing a huge challenge to the already burdened city’s waste management system. Following the special treatment required for BMW due to associated population health and environmental risks, the gap in infrastructural development and the search for pathways to address the challenge, this position paper, examines the evolution of legal framework for biomedical wastes management, related health and environmental issues and policy and program options in the city. Historically, Senegal has ratified many international treaties, including Basel, Stockholm, and Bamako Conventions; however, the paper demonstrates a lack of an efficient chain for BMW disposal in the city. The triangulation of secondary data sources, including implementation evidence, and recent qualitative and quantitative study highlights the disconnections between multiple legal and policy commitments and their efficient implementation, with major barriers attributed to lack of financial resources and weak law enforcement, not only for BMW but solid waste in general. The evidence calls for significant investments for an effective BMW management to address environmental contamination, human exposure and associated loss to health in Dakar and implementation lessons for other Global South municipal actors.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".