Protocol for READY2Exit: a patient-oriented, mixed methods study examining transition readiness in adolescents with co-occurring physical and mental health conditions
Bibliographic record
Abstract
Abstract Background Up to 57% of adolescents and young adults (AYA) with chronic physical health conditions experience mental health conditions, the presence of which contributes to increased morbidity and poor quality of life. AYA with co-occurring physical and mental health conditions, therefore, may experience additional challenges as they transition from pediatric to adult services. While transition readiness – the acquisition of self-management and advocacy skills – contributes to successful transitions to adult care, this concept has not been adequately explored for AYA with co-occurring physical and mental health conditions. Research is needed to identify whether the presence of a mental health comorbidity is associated with transition readiness, and what the experiences of AYA with co-occurring conditions are as they exit pediatric services. This paper outlines the protocol for the R eadiness and E xperiences of AD olescents and Y oung Adults with Co-occurring Physical and Mental Health Conditions Exit ing Pediatric Services (READY2Exit) study; the first study to address this gap using a patient-oriented, mixed methods design. Methods A sequential explanatory mixed methods design will be used to understand the transition readiness of 16–21 year olds with physical and mental health conditions using quantitative and qualitative data. First, Transition Readiness Assessment Questionnaire (TRAQ) scores will be compared among AYA with chronic health conditions, with and without mental health comorbidity. Interviews will then be conducted with approximately 15 AYA with co-occurring health and mental health conditions and analyzed using qualitative description. The READY2Exit study will be conducted in collaboration with five Young Adult Research Partners (YARP) aged 18–30 with lived experience in the health/mental health systems across Canada. The YARP will partner in key tasks such as interview guide co-design, data interpretation, and knowledge translation tool development. Discussion AYA with co-occurring physical and mental health conditions may have unique needs as they prepare for health care transitions. The results of this study will inform the refinement of transition readiness practices to improve care for this group. The active involvement of the YARP across study phases will bring the critical perspectives of young adults to READY2Exit, ensuring the methods, research approaches and outputs align with their needs.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".