Impact of parents' education on variation in hospital admissions for children: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: To assess the impact of parental educational level on hospital admissions for children, and to evaluate whether differences in parents' educational level can explain geographic variation in admission rates. DESIGN: National cohort study. SETTING: The 18 hospital referral areas for children in Norway. PARTICIPANTS: All Norwegian children aged 1-16 years in the period 2008-2016 and their parents. MAIN OUTCOME MEASURES: Age- and gender-adjusted admission rates and probability of admission. RESULTS: Of 1 538 189 children, 156 087 (10.2%) had at least one admission in the study period. There was a nearly twofold (1.9) variation in admission rates between the hospital referral areas (3113 per 100 000 children, 95% CI: 3056 to 3169 vs 1627, 95% CI: 1599 to 1654). Area level variances in multilevel analysis did not change after adjusting for parental level of education. Children of parents with low level of education (maternal level of education, low vs high) had the highest admission rates (2016: 2587, 95% CI: 2512 to 2662 vs 1810, 95% CI: 1770 to 1849), the highest probability of being admitted (OR: 1.18, 95% CI: 1.16 to 1.20), the highest number of admissions (incidence rate ratio: 1.05, 95% CI: 1.01 to 1.10) and admissions with lower cost (-0.5%, 95% CI: -1.2% to 0.3%). CONCLUSIONS: Substantial geographic variation in hospital admission rates for children was found, but was not explained by parental educational level. Children of parents with low educational level had the highest admission probability, and the highest number of admissions, but the lowest cost of admissions. Our results suggest that the variation between the educational groups is not due to differences in medical needs, and may be characterised as unwarranted. However, the manner in which health professionals communicate and interact with parents with different educational levels might play an important role.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".