Informing the ‘early years’ agenda in Scotland: understanding infant feeding patterns using linked datasets
Bibliographic record
Abstract
BACKGROUND: Providing infants with the 'best possible start in life' is a priority for the Scottish Government. This is reflected in policy and health promotion strategies to increase breast feeding, which gives the best source of nutrients for healthy infant growth and development. However, the rate of breast feeding in Scotland remains one of the lowest in Europe. Information is needed to provide a better understanding of infant feeding and its impact on child health. This paper describes the development of a unique population-wide resource created to explore infant feeding and child health in Scotland. METHODS: Descriptive and multivariate analyses of linked routine/administrative maternal and infant health records for 731,595 infants born in Scotland between 1997 and 2009. RESULTS: A linked dataset was created containing a wide range of background, parental, maternal, birth and health service characteristics for a representative sample of infants born in Scotland over the study period. There was high coverage and completeness of infant feeding and other demographic, maternal and infant records. The results confirmed the importance of an enabling environment--cultural, family, health service and other maternal and infant health-related factors--in increasing the likelihood to breast feed. CONCLUSIONS: Using the linked dataset, it was possible to investigate the determinants of breast feeding for a representative sample of Scottish infants born between 1997 and 2009. The linked dataset is an important resource that has potential uses in research, policy design and targeting intervention programmes.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".