Health inequalities in Britain: continuing increases up to the end of the 20th century
Bibliographic record
Abstract
Socioeconomic inequalities in premature mortality in Britain increased over the second half of the 20th century, particularly from the early 1970s onwards.1 The magnitude of mortality differentials reflects the trend in income inequality, which has also undergone a dramatic increase over the past quarter century.1 The present British government have emphasised their commitment to reducing health inequalities. For example the Minister of Health, Alan Milburn, has stated that “Our ambition is to do something that no government—Tory or Labour—has ever done. Not only to improve the health of the nation, but also to improve the health of the worst off at a faster rate”.2 A set of targets for the reduction of health inequalities has been presented. To monitor progress in this regard we have produced updated analyses of premature mortality rates running through to the end of 1999. The mortality data are the Office for National Statistics digital records of all deaths in England and Wales, and equivalent records from the General Register Office (Scotland). The full postcode of the usual residence of the deceased was used to assign each death to the parliamentary constituency in which the deceased usually lived. The death data were provided for single years since 1990 and have been grouped into two year …
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".