Transition for Youth with Sickle Cell Disease: Qualitative Perspectives
Bibliographic record
Abstract
Sickle cell disease (SCD) presents unique challenges for youth transitioning from pediatric to adult health care systems. This study aimed to identify and address unmet needs in transition readiness for youth with SCD in Ontario through participatory design sessions involving patients, health care providers (HCPs), families, and caregivers. Thematic analysis of a participatory design session revealed significant barriers to successful transition: lack of education and awareness among adult HCPs, limitations in health care delivery, navigating multiple life transitions, and racial bias in health care. These barriers contribute to delayed or inadequate care, exacerbating the challenges faced by youth with SCD during the transition period. Building upon these findings, a low-fidelity prototype was developed, culminating in a digital educational module framework tailored for HCPs focusing on SCD transition care. This framework aims to equip providers with the knowledge and resources needed to effectively support transitioning youth with SCD. Successful transition is vital for the health and wellbeing of youth with SCD, and addressing the identified barriers through comprehensive interventions is essential for optimizing transition experiences and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".