Improving the quality of health services in developing countries: lessons for the West
Bibliographic record
Abstract
The West can learn from the experiences of developing countries on improving quality and safety. Quality methods used in health care have been developed in Western health systems. Here there is a growing awareness of the waste and risks caused by problems rooted in systems of care which are not well organised. Governments and others are making resources available to address these problems, and this is being seen as a necessary investment to save money and unnecessary patient suffering. In contrast, in lower income countries the development and quality of health services is severely limited by lack of resources and knowledge about quality methods. Despite these differences, however, lower income countries increasingly recognise the value of quality methods and the need to raise the quality of their services. Many are making more use of quality methods, but the traffic is not one way—the West can also learn from their experiences of improving quality and safety. It is worth remembering that quality methods were first developed and put into widespread use in Japan after the Second World War—a country with few resources—and then re-imported into the West. This editorial considers some of the challenges in applying and adapting quality methods in these countries, as well as the potential for testing and developing more cost effective methods, some of which may be valuable for Western health care. There are severe limitations to health care in most developing countries. One perhaps extreme example from a current programme in a low income Arabic country is presented here. The average spend on public health care per head of population is $6 a year, and it is falling every year. Although there are many health facilities, the services are unevenly distributed and there is a lack of many essential drugs (despite various programmes to solve this …
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 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 | 0.099 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".