Assessment of Disease‐related Knowledge Among Children With Inflammatory Bowel Disease and their Family Using IBD‐KID2
Bibliographic record
Abstract
Background: Children with inflammatory bowel disease (IBD) and their families benefit from improved knowledge of their disease and treatment. Knowledge levels of individual family members are infrequently studied but may identify where education is best directed. We aimed to assess disease‐specific knowledge among children with IBD, parents, and siblings, using a validated assessment tool (IBD‐KID2), and to establish generalizability of IBD‐KID2. Methods: Children with IBD and family members were recruited from tertiary IBD clinics in New Zealand, Australia, and Canada. All participants completed IBD‐KID2 online at baseline, and the children with IBD again after 2 weeks to assess reliability. Results: Participants included 130 children with IBD, 118 mothers, 55 fathers, and 37 siblings. Children with IBD had a mean score of 9.1 (SD 2.9) (maximum 15 points), significantly lower than parents (P < 0.005) and higher than siblings (P < 0.005). Scores of children with IBD were positively associated with current age (P < 0.005), age at diagnosis (P = 0.04) and fathers education level (P = 0.02). Significant score correlations were seen between children with IBD and their mother (P < 0.005) but not father. Sibling scores were not correlated with either parent. Test–retest reliability was high. The cohorts from each country were comparable, and no difference in group scores was seen between countries. Conclusion: IBD‐KID2 is a generalizable and reliable tool for the assessment of disease and treatment knowledge for children with IBD and their families. Score correlations between parents and children with IBD suggest transfer of knowledge, but sibling knowledge is low and targeted education may be beneficial.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".