Assessing Competencies, Needs, and Satisfaction With the Transition From Pediatric to Adult Health Care in Rheumatology: Development and Validation of the Transition-KompAZ
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop and psychometrically validate a self-report instrument to assess (1) competencies, (2) needs, and (3) satisfaction among youth transitioning from pediatric to adult rheumatology. METHODS: The Transition-KompAZ was developed in several steps with conceptual and psychometric analyses. To test its psychometric properties, the instrument was administered to adolescents and young adults (AYAs; 16-25 years) with inflammatory rheumatic diseases before (group 1) or after (group 2) transfer to adult rheumatology. A 2-factor, higher-order confirmatory factor analysis (CFA) model was applied to test the hypothesized factor structure. Internal consistency was estimated using the approach of Raykov with the factor loadings and error variances estimated in the CFA. Spearman rank correlation coefficients were used to assess construct validity. RESULTS: The Transition-KompAZ includes the following modules: (1) competencies in transition (knowledge, self-management), (2) needs (healthcare services, information), and (3) satisfaction (general, transitional care). A total of 173 AYAs (group 1: n = 86; group 2: n = 87) from 12 rheumatology sites completed the Transition-KompAZ. It showed good model fit (comparative fit index > 0.9; Tucker-Lewis index > 0.9; weighted root mean square residual < 0.9) with good internal consistency. The instrument demonstrated moderate-to-good construct validity and good test-retest reliability. CONCLUSION: The Transition-KompAZ appears to be a reliable tool for assessing important dimensions of transition. It may support a structured and individualized transition, as well as the evaluation of transition services. However, further studies are required to assess its predictive value in terms of transfer readiness and successful transition.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".