Getting ready for transition to adult care: Tool validation and multi‐informant strategy using the Transition Readiness Assessment Questionnaire in pediatrics
Bibliographic record
Abstract
BACKGROUND: Transitioning from pediatric to adult healthcare can be challenging and lead to severe consequences if done suboptimally. The Transition Readiness Assessment Questionnaire (TRAQ) was developed to assess adolescent and young adult (AYA) patients' transition readiness. In this study, we aimed to (1) document the psychometric properties of the French-language version of the TRAQ (TRAQ-FR), (2) assess agreements and discrepancies between AYA patients' and their primary caregivers' TRAQ-FR scores, and (3) identify transition readiness contributors. METHODS: French-speaking AYA patients (n = 175) and primary caregivers (n = 168) were recruited from five clinics in a tertiary Canadian hospital and asked to complete the TRAQ-FR, the Pediatric Quality of Life Inventory™ 4.0 (PedsQL™ 4.0), and a sociodemographic questionnaire. The validity of the TRAQ-FR was assessed using confirmatory factor analyses (CFA). Agreements and discrepancies were evaluated using intraclass correlation coefficients and paired-sample t tests. Contributors of transition readiness were identified using regression analyses. RESULTS: The five-factor model of the TRAQ was supported, with the TRAQ-FR global scale showing good internal consistency for both AYA patients' and primary caregivers' scores (α = .85-.87). AYA patients and primary caregivers showed good absolute agreement on the TRAQ-FR global scale with AYA patients scoring higher than primary caregivers (ICC = .80; d = .25). AYA patients' age and sex were found to be contributors of transition readiness. CONCLUSIONS: The TRAQ-FR was found to have good psychometric properties when completed by both AYA patients and primary caregivers. Additional research is needed to explore the predictive validity and clinical use of the TRAQ-FR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".