Disease-Modifying Therapies for Sickle Cell Disease: Decisional Needs and Supports Among Adolescents and Young Adults
Bibliographic record
Abstract
Introduction: Adolescents and young adults (AYAs) with sickle cell disease (SCD) often experience significant challenges in disease management. Hydroxyurea and several newer disease-modifying therapies have demonstrated health benefits; however, uptake of these therapies remains suboptimal. Shared decision-making is one promising solution to addressing barriers in uptake and to improve health outcomes for AYAs with SCD. A thorough understanding of AYA's decisional needs can guide the development of tailored decisional supports and promote shared decision-making. Informed by the Ottawa Decision Support Framework, this study aimed to examine decisional needs and supports reported by AYAs with SCD, their caregivers, and SCD healthcare providers. Methods: Semi-structured qualitative interviews were conducted with AYAs ages 15-25 years with SCD ( n=14) and caregivers of AYAs with SCD ( n=11) receiving care at two hospitals in the mid-Atlantic region of the US. Qualitative data were also obtained through online crowdsourcing with SCD providers across the US ( n=40). Most AYAs (93%) identified as non-Hispanic Black ( M age=21 years, 57% male) and had SS genotype (79%). Most caregivers were female (80%) and all identified as non-Hispanic Black (100%). About two-thirds of healthcare providers were female (65%) and non-Hispanic White (65.0%), with an average of 14.8 years in practice ( range=2-57 years). Thematic and descriptive content analyses were used to summarize perspectives on decisional needs and supports regarding disease-modifying therapies. Adequate inter-rater reliability (Cohen's Kappas > .80) and thematic saturation were achieved. Results: AYAs and caregivers reported needing six areas of support when deciding on disease-modifying therapies: resolving decisional conflicts, gaining knowledge, clarifying expectations, garnering supports and resources, navigating complexities in the decision, and addressing other personal concerns. Families reported receiving supports from providers that facilitated their decision-making by helping them: gain knowledge, clarify expectations, address other personal concerns, build rapport with providers, and resolve unreceptiveness or ambivalence to making a decision. Providers reported offering additional supports targeting: inadequate experience in implementing therapies (e.g., pill swallowing), inadequate health and social services and financial assistance, and inadequate instrumental support (e.g., transportation). Conclusions: This research highlights the needs AYAs and caregivers identify when making decisions about disease-modifying therapies for SCD and the decision supports offered by healthcare providers. Decisional needs reported by families generally corresponded to supports offered by providers. Providers also highlighted additional supports that may be helpful for families (e.g., instrumental supports, such as financial, emotional, and skillset assistance). Findings will inform the development of a shared decision-making intervention for AYAs with SCD, their caregivers, and healthcare providers. Future research is needed to examine which decision supports may be most impactful to promote shared decision-making for disease-modifying therapies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".