Improving the Transition from Pediatric to Adult Healthcare: Recommendations from Young Adults with Lived Experience
Bibliographic record
Abstract
Background: The process of transitioning from pediatric to adult healthcare is a pivotal juncture in the overall life course of young adults (YAs) with complex care needs (CCN) and their families. As service demands increase for this population, healthcare providers must determine the best ways to meet their needs. Following the transition to adult healthcare, YAs are vulnerable to poor outcomes, such as increased stress and repeated ER visits. Studies that explore how to address their needs often do not reflect the perspectives of YAs. As such, seeking their input about health services is a crucial step towards improving service delivery. Objective: This study aimed to explore the experiences and recommendations of YAs with CCN to improve the transition process from pediatric to adult healthcare across settings and sectors. Methods: A qualitative descriptive design was used. Semi-structured interviews were conducted with 23 young adults aged 19–29 with CCN from a small Canadian province. Maximum variation sampling was used to capture diverse perspectives from individuals with an array of CCN. Data was managed using NVivo software and analyzed using inductive thematic analysis. Results: The following themes were identified in the data, highlighting the importance of: 1) continuity of care, 2) improved access to care and supports, 3) transition readiness, and 4) a patient-centred care team. Conclusion: Results from this study can inform practice, policy, and research by guiding the development of service delivery improvement strategies for YAs and their families involved in the transition from pediatric to adult healthcare.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".