Response pattern analysis of IBD‐KID: A knowledge assessment tool for children with inflammatory bowel disease
Bibliographic record
Abstract
AIM: Paediatric inflammatory bowel disease (IBD) is a chronic relapsing condition requiring adherence to complex treatment regimens to achieve best outcomes. Adherence is frequently low in this population but can be improved by increasing disease- and treatment-related knowledge. The IBD-knowledge inventory device (IBD-KID) is a knowledge assessment tool specifically developed and validated for children with IBD. To analyse IBD-KID participant response patterns in order to review the strength of the tool. METHODS: A cohort of children with IBD completed IBD-KID, and their responses were used to assess the tool's validity and feasibility. Item response analysis assessed the item difficulty and the ability of items to discriminate between high/low scorers. The analysis considered item structure, readability and the effectiveness of multiple choice items. RESULTS: A total of 105 completed IBD-KID assessments showed that 12 items (52%) had an acceptable difficulty level, and 17 (74%) were effective at discriminating between high/low scorers. Nine (61%) had good readability, but comprehension levels ranged from 5 to 18 years. Seven (30%) had elevated 'don't know' responses, highlighting the need for content and construction review. Of the 10 multiple choice items, 9 were complex and not functioning efficiently. Internal consistency was acceptable but could be improved by removing two items. CONCLUSIONS: The response analysis metrics were reviewed by an expert panel and provided a framework for IBD-KID improvements with the aim of increasing discrimination and reducing difficulty without adversely affecting reliability. The proposed revisions will address components that may have caused children to answer incorrectly due to confusion rather than lack of knowledge.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".