40 years after Alma-Ata, is building new hospitals in low-income and lower-middle-income countries beneficial?
Bibliographic record
Abstract
Public hospitals in low-income and lower-middle-income countries face acute material and financial constraints, and there is a trend towards building new hospitals to contend with growing population health needs. Three cases of new hospital construction are used to explore issues in relation to their funding, maintenance and sustainability. While hospitals are recognised as a key component of healthcare systems, their role, organisation, funding and other aspects have been largely neglected in health policies and debates since the Alma Ata Declaration. Building new hospitals is politically more attractive for both national decision-makers and donors because they symbolise progress, better services and nation-building. To avoid the 'white elephant' syndrome, the deepening of within-country socioeconomic and geographical inequalities (especially urban-rural), and the exacerbation of hospital-centrism, there is an urgent need to investigate in greater depth how these hospitals are integrated into health systems and to discuss their long-term economic, social and environmental sustainability.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".