Biomedical waste management in Dakar, Senegal: legal framework, health and environment issues; policy and program options
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it