Application of Rasch analysis to the parent adherence report questionnaire in juvenile idiopathic arthritis
Bibliographic record
Abstract
BACKGROUND: Adherence to treatment in children with juvenile idiopathic arthritis (JIA) is associated with better outcomes. Assessing patient adherence in JIA, as well as attitudes and beliefs about prescribed treatments, is important for the clinician in order to optimize patient management. The objective of the current study was to evaluate the psychometric properties of the Parent (proxy-report) Adherence Report Questionnaires (PARQ), which assesses beliefs and behaviors related to adherence to treatments prescribed for JIA. METHODS: A Rasch analysis was conducted on data collected with parents of children with JIA from two studies in which the PARQ was used as a measure of adherence. RESULTS: The PARQ showed preliminary evidence of multidimensionality with two factors, accounting for 38 % and 27 % of the variance respectively. The PARQ in its original version does not adhere to expectations of the Rasch model. A transformed version of the PARQ obtained by deletion of the general adherence scale and modification of visual analog scales into 5-point likert scales improved fit to the model and showed preliminary evidence of unidimensionality. CONCLUSIONS: The PARQ was transformed based on the results of the Rasch analysis. The transformed version of the PARQ shows preliminary evidence of unidimensionality and may allow computation of a total score, although further testing is needed to verify these findings.
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".