Building on Julian Tudor Hart’s example of anticipatory care
Bibliographic record
Abstract
The prevention and delay of chronic disease is an increasing priority in all advanced health-care systems, but sustainable, effective and equitable approaches remain elusive. In a famous pioneering example in the UK, Julian Tudor Hart combined reactive and anticipatory care within routine consultations in primary medical care, while applying a population approach to delivery and audit. This approach combined the structural advantages of UK general practice, including universal coverage and the absence of user fees, with his long-term commitment to individual patients, and was associated with a 28% reduction in premature mortality over a 25-year period. The more recent, and comprehensively evaluated Scottish National Health Service demonstration project, 'Have a Heart Paisley', took a different approach to cardiovascular prevention and health improvement, using population screening for ascertainment, health coaches and referral to specific health improvement programmes for diet, smoking and exercise. We draw from both examples to construct a conceptual framework for anticipatory care, based on active ingredients, programme pathways and whole system approaches. While the strengths of a family practice approach are coverage, continuity, co-ordination and long-term relationships, the larger health improvement programme offered additional resources and expertise. As theory and evidence accrue, the challenge is to combine the strengths of primary medical care and health improvement, in integrated, sustainable systems of anticipatory care, addressing the heterogeneity of individual needs and solutions, while achieving high levels of coverage, continuity, co-ordination and outcome.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".