Multisectoral approaches to addressing global urban maternal and perinatal health inequities
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
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.
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 | 0.002 | 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 | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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