Developing a transition workshop for adolescents with sickle cell disease
Bibliographic record
Abstract
Background: The transfer from paediatric to adult care presents a significant challenge for adolescents and young adults (AYA) with sickle cell disease (SCD). Disease self-management skills have been recognized as important mediators of poor health outcomes, but transition-related skills such as scheduling appointments and understanding the shift in health care responsibilities remain under taught in the healthcare system. The purpose of this quality improvement (QI) study was to (1) understand the areas of disease self-management that AYA patients felt underprepared for, and (2) design and evaluate an educational program addressing the top unmet skills. Methods: The Plan-Do-Study-Act (PDSA) model was implemented in a tertiary outpatient clinic between September 2020 and July 2021. Patients were surveyed during regular clinic visits to identify their priorities on disease self-management skills. Based on the results, a virtual workshop was created and administered three separate times. Feedback was collected using an electronic survey. Results: 'Navigating the adult health care system' was the top self-management skill that AYA patients (n = 39) identified needing more support with. The majority of participants (79%, n = 14) reported that the virtual workshop was excellent, with 'appointment management' being the most useful topic discussed. Participants also reported feeling more confident (71%) and aware (86%) of how to navigate adult care. Conclusion: This QI study showed that delivering a virtual workshop on transition skills was well received by AYA participants with SCD. The PDSA framework was useful for identifying specific knowledge gaps in transition aged AYA to help improve patient education in a well-established transition program.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".