Understanding the slum–health conundrum in sub-Saharan Africa: a proposal for a rights-based approach to health promotion in slums
Bibliographic record
Abstract
Sub-Saharan Africa is the world's least urbanized region but is ironically also the region with the largest proportion of urban slum dwellers. However, there exists limited understanding of the impact of slums on health in the region. To address this knowledge gap, we conducted a systematic search in PubMed, Google, and Google Scholar to identify and review studies examining the slum-health relationship in sub-Saharan African cities. Subsequently, we performed thematic analysis of 40 studies to identify themes that explain the health impact of slums in the region. The majority of studies characterize slums as health-damaging settings, where poverty and unfavorable environmental conditions pose threats to public health and safety. Only a handful of studies suggest a beneficial relationship between slums and health, in such areas as affordable housing provision, employment generation, and community cohesion. We argue that the literature's overwhelming emphasis on the environmental risks of slums feeds into a neoliberal urban agenda that seeks to clear slums at the expense of their beneficial contributions to health. Accordingly, we advocate a shift in policy discourse, from static characterization of slums as health risks to a health-promotion agenda that emphasizes the housing and service rights of slum populations.
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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 | 0.065 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.017 | 0.045 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.012 | 0.016 |
| 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".