Urban Family Planning in Sub-Saharan Africa: an Illustration of the Cross-sectoral Challenges of Urban Health
Bibliographic record
Abstract
The multi-sectoral nature of urban health is a particular challenge, which urban family planning in sub-Saharan Africa illustrates well. Rapid urbanisation, mainly due to natural population increase in cities rather than rural-urban migration, coincides with a large unmet urban need for contraception, especially in informal settlements. These two phenomena mean urban family planning merits more attention. To what extent are the family planning and urban development sectors working together on this? Policy document analysis and stakeholder interviews from both the family planning and urban development sectors, across eight sub-Saharan African countries, show how cross-sectoral barriers can stymie efforts but also identify some points of connection which can be built upon. Differing historical, political, and policy landscapes means that entry points to promote urban family planning have to be tailored to the context. Such entry points can include infant and child health, female education and employment, and urban poverty reduction. Successful cross-sectoral advocacy for urban family planning requires not just solid evidence, but also internal consensus and external advocacy: FP actors must consensually frame the issue per local preoccupations, and then communicate the resulting key messages in concerted and targeted fashion. More broadly, success also requires that the environment be made conducive to cross-sectoral action, for example through clear requirements in the planning processes' guidelines, structures with focal persons across sectors, and accountability for stakeholders who must make cross-sectoral action a reality.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".