Bibliographic record
Abstract
The world's population has doubled over the past 50 years.1 The annual growth rate of 1.3% will result in a further increase to around 9 billion by 2050. Nearly a third of this growth is attributed to three countries in South Asia—namely India (21%), Pakistan (5%), and Bangladesh (4%)—which have historic, cultural, and economic ties with the United Kingdom. According to the International Organisation for Migration, the total number of migrants worldwide increased from 84 million in 1975 to 175 million by 2000,2 and by 2050 it may have reached 230 million. Meanwhile, the global population of elderly people is increasing. By 2050 the overall growth rate of 2.4% per year will result in a threefold increase in the number of people aged 60 or older to 2 billion, with eight out of every 10 elderly people living in developing countries.3 Large demographic changes will occur in Europe.4 The current population of the European Union of 452 million will shrink to around 400 million despite its current inward migration rate. Populations in some European countries will decrease by a quarter while becoming considerably older. By 2050 the proportion of elderly people is expected to have risen from 20% to 37%, with a big impact on Europe's economies and social infrastructures. These trends in international migration and population ageing will probably increase the influx of South Asians to the United Kingdom. Many will bring elderly relatives with them given that, in Asian countries, 70% of elderly people live with their children. In the UK over the past decade the ethnic minority population has grown by 53% and now comprises 7.9% of the total population.5 South Asians, the largest ethnic minority group, now number two and a half million people and account for 50% of ethnic minority groups, with another 15% of the ethnic population described as of mixed race. Although increasing immigration may provide a welcome solution to such shrinking and ageing among Europe's populations6 it will almost certainly have a substantial impact on health services such as the NHS, because South Asians have higher rates of coronary heart disease, diabetes, hypertension, stroke, hip fractures, and renal failure.7,8 So what needs to be done? The European Union must encourage managed migration. The union needs cohesive policies for immigration and health which can respond properly to the medical needs of the migrant population. First, though, policy makers should assess the likely effects of further migration on health services before enforcing big changes in the numbers of migrants. Ill conceived and short sighted attempts to develop services could prove to be a disastrous knee jerk reaction. The UK currently allows in 150 000 migrants a year. Those in charge of developing and modernising the NHS should take account of the rapidly changing demography of the nation, understand better the needs of ethnic minority populations, and target health promotion at people in those populations who are at high risk of disease. Basic and postgraduate training for doctors, nurses, and professions allied to medicine must include learning about ethnic diversity and transcultural medicine, while academics must more widely debate and develop capacity for clinical research in transcultural medicine.9 Meanwhile, royal colleges, specialist societies, voluntary organisations, patients' groups, and community leaders could do much more to promote and share expertise on the health of people from ethnic minorities. Lastly, exchange programmes for health professionals in the UK and less developed countries would allow dissemination and adaptation of the UK's substantial knowledge in managing diseases of old age and chronic diseases, as well as of health service finance and management.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".