A qualitative study on the educational needs of young people with chronic conditions transitioning from pediatric to adult care
Bibliographic record
Abstract
OBJECTIVES: Although patient education is recommended to facilitate the transition from pediatric to adult care, a consensus has not been reached for a particular model. The specific skills needed for the transition to help in facilitating the life plans and health of young people are still poorly understood. This study explored the educational needs of young people with diverse chronic conditions during their transition from pediatric to adult care. METHODS: Qualitative semi-structured interviews were conducted with 17 young people with chronic conditions. A thematic analysis was conducted to examine the data. RESULTS: Five themes emerged from the data, identified through the following core topics: learning how to have a new role, learning how to adopt a new lifestyle, learning how to use a new health care service, maintaining a dual relationship with pediatric and adult care, and having experience sharing with peers. CONCLUSION: A shift in perspective takes place when the transition is examined through the words of young people themselves. To them, moving from pediatric to adult care is not viewed as the heart of the process. It is instead a change among other changes. In order to encourage a transition in which the needs of young people are met, educational measures could focus on the acquisition of broad skills, while also being person-centered.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".