Multisectoral approaches to addressing global urban maternal and perinatal health inequities
Bibliographic record
Abstract
Emerging trends show declines in maternal and perinatal mortality and morbidity in urban populations might be slower than in rural areas in a variety of contexts. This is happening at a critical juncture in time when urban populations are rapidly increasing and might be partly driven by specifics of vulnerability of the urban poor in Low-income countries and High-income countries alike. Poor maternal and perinatal health outcomes are largely preventable but focusing solely on healthcare interventions misses critical opportunities to reduce ill-health. Social and environmental determinants such as poverty and the impact of climate change must be integrated into policy decisions, especially to benefit poor urban dwellers. Integrating data on the social determinants of health into policy decisions can help multisectoral stakeholders embrace a more Health-in-all-policy approach creating opportunities for better outcomes for these urban poor women and their offspring. We provide examples of two cities – Rotterdam and Kampala – to show that successful multi-sectoral approaches that can address urban maternal and perinatal inequalities should focus on interventions in which healthcare and non-healthcare determinants are integrated.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".