Value‐added education and smoking uptake in schools: a cohort study
Bibliographic record
Abstract
AIM: To show that schools achieving higher examination pass and lower truancy rates than expected, given that their pupil populations (high value-added schools) are associated with a lower incidence of smoking among pupils (13-14 years). DESIGN: Value-added scores for schools were derived from standardized residuals of two regression equations predicting separately the proportion of pupils passing high school diplomas and the half-days lost to truancy from the socio-economic and ethnic profiles of pupils. The risk of regular smoking at 1- and 2-year follow-up was examined in relation to the value-added score in a cohort of 8352 UK pupils. Random-effects logistic regression was used to adjust for baseline smoking status and other adolescent smoking risk factors. SETTING: A total of 52 schools, West Midlands, UK. PARTICIPANTS: Year 9 pupils aged 13-14 years (n = 8352) were followed-up after 1 year (n = 7444; 89.1% of original cohort) and 2 years (n = 6819; 84.6% of original cohort excluding pupils from two schools that dropped out). MEASUREMENTS: Regular smoking (at least one cigarette per week). FINDINGS: Schools with high value-added scores occurred throughout the socio-demographic spectrum. The odds ratio (95% confidence interval) for regular smoking for a 1 standard deviation increase in the value-added measure was 0.85 (0.73-0.99) at 1-year and 0.80 (0.71-0.91) at 2-year follow-ups. Baseline smoking status did not moderate this. CONCLUSIONS: Schools with high value-added scores are associated with lower incidence of smoking. Some schools appear to break the strong link between deprivation and smoking. Understanding the mechanisms could be of great public health significance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".