Youth, Caregiver, and Provider Perception of the Transition from Pediatric to Adult Care for Youth with Chronic Diseases
Bibliographic record
Abstract
OBJECTIVE: Youth with chronic diseases are required to transition from pediatric to adult care across the world at variable ages in their adolescent years. The aim of this study is to examine perspectives of young patients, caregivers, and physicians in the transition process. METHODS: This 3-phase mixed methods research study gathered data using an iterative approach with the collaboration of youth coresearchers. Physician opinions were gathered through a survey. Further data were collected through 15 semistructured, standardized interviews of adolescent medicine physicians. Perspectives of adolescents and young adult (AYA) patients and their caregivers were gathered independently using a 20-item survey. Quantitative data were analyzed with descriptive statistics and sorted by theme. RESULTS: In phase 1, respondents rated current transition processes as an average 5.19/10 on a 10-point Likert scale (1 = poor and 10 = excellent) with no participants rating 9 or 10 of 10. The top barrier identified was a lack of communication between pediatric and adult doctors (71.0%). The top ranked strategy for improvement was to provide formal transition guidelines (69.8%). In phase 2, specific concerns include lack of insurance coverage, lack of physicians available to take on youth transitioning to adult care who are also knowledgeable regarding pediatric conditions, and lack of funding or staff support for transition clinics. In phase 3, most of the youth surveyed (52%) reported that their physicians have not involved them in conversations about transitioning. AYA patients prefer the point of transfer to occur with other life transitions such as graduation, and caregivers prefer transfer to happen during times of stability where their children can dedicate adequate time to their health. CONCLUSION: Fulfilling youth desire for increased patient autonomy and ownership can help overcome their poor perception of the transition process. Increased physician training in adolescent health and improvements in post-transition community plans may be beneficial to prevent loss to follow-up among young patients across medical disciplines. Successful transition into adult care requires a unique process for each youth and requires adequate preparation from the pediatric front, empowerment of young patients and their families, and continuity of care by adult providers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".