Supply-side and demand-side factors affecting allopathic primary care service delivery in low-income and middle-income country cities
Bibliographic record
Abstract
Most people in low-income and middle-income countries (LMICs) now live in cities, as opposed to rural areas where access to care and provider choice is limited. Urban health-care provision is organised on very different patterns to those of rural care. We synthesise global evidence to show that health-care clinics are plentiful and easily accessible in LMIC cities and that they are seldom overcrowded. The costs that patients incur when they seek care are highly variable and driven mostly by drugs and diagnostics. We show that citizens have agency, often bypassing cheaper facilities to access preferred providers. Primary care service delivery in cities is thus best characterised as a market with a diverse range of private and public providers, where patients make active choices based on price, quality, and access. However, this market does not deliver high-quality consultations on average and does not provide continuity or integration of services for preventive care or long-term conditions. Since prices play a key role in accessing care, the most vulnerable groups of the urban population often remain unprotected.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".