Exploring interaction effects of social determinants of health with hospital admission type on academic performance: a data linkage study
Bibliographic record
Abstract
OBJECTIVE: To investigate the moderating effects of socio-demographic social determinants of health (SDH) in the relationship between types of childhood hospitalisation (ie, none, injury, non-injury, injury+non-injury) and academic performance. DESIGN, SETTING AND PATIENTS: Children residing in Wales 2009-2016 (N=369 310). Secure Anonymised Information Linkage databank linked Tagged Electronic Cohort Cymru (five data sources) from the Wales Electronic Cohort for Children. MAIN OUTCOME MEASURE: Binary educational achievement (EA) measured across three key educational stage time points: grade 6 (mean age 11 years, SD 0.3), 9 (mean age 14 years, SD 0.3) and 11 (mean age 16 years, SD 0.3). RESULTS: Of the 369 310 children, 51% were males, 25.4% of children were born in the lowest two Townsend deciles. Females were more likely to meet EA than males (adjusted risk ratio (aRR) (95% CI): 1.047 (1.039, 1.055)). EA was lower for injury admissions in males and any admission type in females (interactions: female×non-injury 0.982 (0.975, 0.989); female×injury+non-injury 0.980 (0.966, 0.994)). Children born into a more deprived decile were less likely to achieve EA (0.979 (0.977, 0.980)) and worsened by an injury admission (interactions: townsend×injury 0.991 (0.988, 0.994); Townsend×injury+non-injury 0.997 (0.994, 1.000)). Children with special educational needs (SEN) were less likely to meet EA (0.471 (0.459, 0.484) especially for an injury admission (interactions: SEN×injury 0.932 (0.892, 0.974)). CONCLUSION: SDH moderated the impact of hospital admission type on educational outcomes prompting future investigation into the viability of in-hospital routine screening of families for SDH and relevant post-hospital interventions to help reduce the impact of SDH on educational outcomes post-hospitalisation.
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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 | 0.047 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".