Personal Views: Reconciling 21st century temptations with 20th century resources and problems
Bibliographic record
Abstract
Healthcare systems in many developing countries face a major challenge: how to meet the demand for 21st century standards of health care and technology with funds that, as a percentage of gross domestic product (GDP), remain lower than what developed nations were investing in health in the 1980s. And, furthermore, how can developing countries meet such expectations when they are still dealing with health problems that rich countries had overcome 40 or 50 years ago? Take my country, for example. In recent years Brazil has been spending some 7-8% of its GDP (both public and private sector investment) on the health sector. Over the past few decades developed nations have been progressively increasing their spending on health. In the 1960s countries such as Canada, France, Switzerland, Australia, Italy, and the United States spent some 4-5% of their GDP on health. By the end of the 1980s this figure had increased to 8-9%. The United States now leads the world: in 2004 it spent some $1.7 trillion, or 15.3% of its GDP, on health. In absolute terms this amount is more than three times the entire Brazilian GDP. Resources in Brazil are far too scarce to think about investing in today's technology Thus, in terms of percentage of GDP Brazil spent the same in 2005 as developed countries were spending in the 1980s. The situation is similar if we look at absolute amounts spent per person in the population. The US spends some nine times more per inhabitant than Brazil does—Switzerland six times more, Germany, Norway, and Canada five times more, and Britain 3-4 times more. Resources in Brazil are far too scarce even to think about giving in to the temptation of investing in today's technology. Such spending cannot be justified, given the number of other areas that require investment for health to be improved. Basic sanitation, education, food, and security are just some that come to mind. Another reason for not spending more on modern technology is the current inefficiencies in our health system. It makes no sense to invest in the latest equipment when the infrastructure isn't in place to support it. As in numerous other developing countries, in Brazil we have all the typical health problems of developed nations (cardiovascular diseases and cancers, among others) but have not yet dealt sufficiently with the sorts of health problem that richer countries resolved 40 or 50 years ago. Diarrhoea, diseases of the respiratory tract, and infectious diseases continue to be major problems, despite the fact that well known and effective preventive measures exist. Some of Brazil's health indicators—infant mortality, low birth weight, life expectancy at birth, and the proportion of the population aged over 60—are still at levels that prevailed in developed countries 40 or 50 years ago. The demand for new technology is understandable, given the plethora of available information and increasing awareness. We all want access to the best practices, especially when we are working with what we hold as most precious, human life. However, in the health sector we deal with biological phenomena. No matter how much evidence we have to support our decisions, the possibility always exists that we may have got them wrong. Medicine is an inexact science in constant evolution, and some of its “truths” are transitory and require caution. Members of the public, who fund the health system, have every right to want access to a good quality service. But we need to recognise that our health system, whether special circumstances of healthcare systems in developing countries at the moment, it is important that all stakeholders—the public, service providers, managers, suppliers, and policy makers and regulators—share responsibility for the stability and continuing viability of health care. The public is responsible for raising questions about the appropriate use of the health system. After all, they are funding the system. Funding and risk managers, along with service providers, are responsible for the appropriate use of the available funds and for the system's quality and efficiency. And finally, the policy makers and inspectors—a small but important group—are responsible for deciding on and implementing health policies, with a view not just to the short and medium terms but also to the long term. The challenge is great, but the opportunities for improvement are even greater, as there is currently much waste of resources in health systems in developing countries. A more efficient health system, with focused policies and transparent and justified decisions, must be our goal. Only then will we be able to demand additional resources.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.016 | 0.033 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".