Bibliographic record
Abstract
In the UK, the national immunisation programme is delivered free at point of care through the National Health Service (NHS) primary healthcare teams, led by general practitioners (GPs), and to a smaller and locally variable extent through child health clinics. Nearly all British children are vaccinated through the NHS, rather than privately. The UK national immunisation programme has been run by Child Health Systems which are in the process of being replaced as part of the National Programme for Information Technology (NPfIT) implemented by Connecting for Health, a Government agency. The Health Protection Agency (HPA) expressed concerns about the first system to be rolled out in August 2005. Since then, some of the problems which have resulted from the new systems have been well publicised, including the inability of systems to track children and calculate vaccination coverage.1 2 3 In London, two systems, the Child Health Interim Application (CHIA) and the Electronic Care record system (RiO) have been implemented without a call and recall function, with clear implications for patient safety. The opportunity was lost to improve immunisation in London, the worst place for this to have happened because London’s coverage is poor — and is the reason that the UK fails to meet its World Health Organization targets on immunisation.4 The city stands in contrast to other European capitals such as Paris, and to other cities in the UK which face similar challenges in having large mobile and deprived populations. London’s bad coverage has gone hand in hand with bad data, and bad data are toxic.5 Immunisation keeps us all healthy. Its global priority is shown by the excess of $1.7 billion given to childhood vaccines by Bill and Melinda Gates in 1999–2007, mainly to the World Health Organization and Global Alliance for Vaccines and Immunization …
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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 | 0.095 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.021 | 0.050 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.046 | 0.067 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".