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Record W4282841278 · doi:10.1007/s11524-022-00649-z

Urban Family Planning in Sub-Saharan Africa: an Illustration of the Cross-sectoral Challenges of Urban Health

2022· article· en· W4282841278 on OpenAlexaff
Trudy Harpham, Moses Tetui, R. E. F. Smith, Ferdinand Okwaro, Adriana A. E. Biney, Judith F. Helzner, James Duminy, Susan Parnell, John Kuumuori Ganle

Bibliographic record

VenueJournal of Urban Health · 2022
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Waterloo
FundersInternational Union for the Scientific Study of PopulationBill and Melinda Gates Foundation
KeywordsGeographyUrban planningRegional scienceEnvironmental planningCivil engineeringEngineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.338
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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