Council tax valuation band predicts breast feeding and socio-economic status in the ALSPAC study population
Bibliographic record
Abstract
BACKGROUND: Breast-feeding rates in the UK are known to vary by maternal socio-economic status but the latter function is imperfectly defined. We test if CTVB (Council Tax Valuation Band - a categorical assessment of UK property values and amenities governing local tax levies) of maternal address predicts, in a large UK regional sample of births, (a) breast-feeding (b) personal and socio-economic attributes of the mothers. METHODS: Retrospective study of a subset (n.1390 selected at random) of the ALSPAC sample (Avon Longitudinal Study of Parents and Children), a large, geographically defined cohort of mothers followed from early pregnancy to 8 weeks post-delivery. Outcome measures are attitudes to breast-feeding prior to delivery, breast-feeding intention and uptake, demographic and socio-economic attributes of the mothers, CTVB of maternal home address at the time of each birth. Logistic regression analysis, categorical tests. STUDY SAMPLE: 1360 women divided across the CTVBs--at least 155 in any band or band aggregation. CTVB predicted only one belief or attitude--that bottle-feeding was more convenient for the mother. However only 31% of 'CTVB A infants' are fully breast fed at 4 weeks of life whereas for 'CTVB E+ infants' the rate is 57%. CTVB is also strongly associated with maternal social class, home conditions, parental educational attainment, family income and smoking habit. CONCLUSION: CTVB predicts breast-feeding rates and links them with social circumstances. CTVB could be used as the basis for accurate resource allocation for community paediatric services: UK breast-feeding rates are low and merit targeted promotion.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".