Impact of contractual financial incentives on the ascertainment and management of smoking in primary care
Bibliographic record
Abstract
BACKGROUND: The April 2004 contract for UK general practitioners (GPs) is an ambitious attempt to produce substantial changes in clinical practice. We investigated the impact of this on delivery of primary care smoking cessation interventions. METHODS: We analysed data from patients' medical records that were held within a large database called The Health Improvement Network (THIN). We calculated for each year between 1990 and 2005 and for each quarter-year from 2003 the incidence of recording of smoking status in medical records and, in smokers, the receipt of GPs' smoking cessation advice and prescriptions for nicotine addiction treatments. FINDINGS: Recording of smoking status increased temporarily around 1993-4 and then rose gradually from the year 2000. This rise was more marked from 2003, with an 88% increase between the first quarters of 2003 and 2004. The latter quarter was just prior to the introduction of the GP contract and higher rates of recording smoking status were sustained for the subsequent year. In smokers, there was a broadly similar pattern for the proportion recorded as having received brief cessation advice. However, while there was a sharp increase in prescriptions for nicotine addiction treatments from 2000, no comparable acceleration in this trend from 2003 was apparent. INTERPRETATION: The 2004 GP contract increased primary care rates of smoking status ascertainment and recording of advice against smoking. The public health impact of this contract could be maximized if it also improved GPs' prescribing of nicotine addiction treatments.
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 | 0.156 | 0.433 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".