Council tax valuation band of patient residence and clinical contacts in a general practice.
Bibliographic record
Abstract
BACKGROUND: There is a dearth of data relating UK general practice workload to personal and social markers of individual patients. AIM: To test whether there is a significant association between general practice patient contact rates and the council tax valuation band of their residential address. DESIGN OF STUDY: Cross-sectional analyses using data recorded, over 1 year, for over 3300 general practice patients. SETTING: One medium-sized group practice in an industrialised English market town. METHOD: Face-to-face contacts between the patients and the doctors and nurses in the practice were compared by patient age, sex, registration period, distance from surgery, Underprivileged Area 8 (UPA8) score, and council tax valuation band. RESULTS: Patient sex, age, recent registration, distance from surgery, and council tax valuation band were each significantly associated with face-to-face contact rate in univariate analyses. UPA8 score was not significantly associated with contact rates. On multivariate testing, sex, age, recent registration, and council tax valuation band remained significantly associated with contact rates. The last is a new finding. CONCLUSION: Council tax valuation bands predict contact rate in general practice; the lower the band, the higher the contact rate. Council tax valuation band could be a useful marker of workload that is linked to socioeconomic status. This is a pilot study and multipractice research is advocated.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".