Using Cognitive Interviewing for Test Items to Assess Physical Function in Children with Cerebral Palsy
Bibliographic record
Abstract
In Brief Purpose: The purpose of this study was to assess the content, format, and comprehension of test items and responses developed for use in a computer adaptive test (CAT) of physical function for children with cerebral palsy (CP). Methods: After training in cognitive interviewing techniques, investigators defined item intent and developed questions for each item. Parents of children with CP (n = 27) participated in interviews probing item meaning, item wording, and response choice adequacy and appropriateness. Results: Qualitative analysis identified 3 themes: item clarity; relevance, context, and attribution; and problems with wording or tone. Parents reported the importance of delineating task components, assistance amount, and environmental context. Conclusion: Cognitive interviewing provided valuable information about the validity of new items and insight to improve relevance and context. We believe that the development of CATs in pediatric rehabilitation may ultimately reduce the impact of the issues identified. Parents interviewed about items for a test of physical function indicated the importance of describing task components, the amount of assistance, and environmental context. The authors believe cognitive interviews allow parents to provide information on the validity of items and insights to improve test items.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".