School Well-Being and Academic Performance of Children With Juvenile Idiopathic Arthritis: A National Register-Based Study
Bibliographic record
Abstract
OBJECTIVE: We aimed to investigate how school well-being (SWB) and academic performance of children with juvenile idiopathic arthritis (JIA) compare to their peers on a national level using the Danish national registers. Further, we investigated the potential influence of socioeconomic status (SES). METHODS: A population-wide, register-based, cross-sectional study was performed. We compared the results of children with and without JIA in the Danish National Well-Being Questionnaire (DNWQ), the National Danish School Testing (NDST), and their ninth grade (aged approximately 16 yrs) final school marks in Danish and mathematics. The results were analyzed using adjusted ordinal logistic regression (SWB) and linear regression (tests and marks). RESULTS: In separate cohorts, we included a total of 505,340 children answering the DNWQ, 812,461 children with NDST results, and the ninth-grade final marks of 581,804 children. Of these children, 1042, 1541, and 1410, respectively, fulfilled the criteria of JIA. Children with JIA reported SWB comparable to their peers, except for the question "Do you perform well in school?" (odds ratio 0.89, 95% CI 0.81-0.99). In the NDST, the children with JIA in general did just as well as their peers. We found no differences in the ninth-grade final marks in either Danish or mathematics. Stratifying the analyses on SES showed no significant differences in the associations. CONCLUSION: Overall, children with JIA report SWB comparable to that of children without JIA and perform equally well in school as children without JIA.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".