Linking Food and Resource Access to Medical Care Access in Maputo, Mozambique
Bibliographic record
Abstract
Background: Rapid urbanization coupled with evolving threats from both communicable and non-communicable diseases underscore the vulnerability of urban healthcare systems. Building resilient healthcare systems and increasing access to socioeconomic resources is key for achieving sustainable development goals (SDGs). The city of Maputo (Mozambique) provides a helpful case study for the analysis of this situation. Methods: This investigation analyzes household survey data to determine the predictors of consistent household medical care access (SDG 3) in Maputo. Using those identified predictors, the study identifies key segments of households in Maputo that are vulnerable to disease given their inconsistent access to medical care. Results: The results indicate that households with inconsistent medical care access (SDG 3) also suffer from severe food insecurity (SDG 2) and inconsistent access to a cash income (SDG 8), water (SDG 6), and electricity (SDG 7). Conclusions: This study identifies challenges to the achievement of SDG 3 in Maputo, where households that are likely to need medical care under the strain of impoverished living conditions are also the least likely to have consistent access to needed medical care.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".