Disease‐modifying therapies for sickle cell disease: Decisional needs and supports among adolescents and young adults
Bibliographic record
Abstract
BACKGROUND: Shared decision-making is one promising solution to addressing barriers in use of disease-modifying therapies for adolescents and young adults (AYAs) with sickle cell disease (SCD). A thorough understanding of decisional needs can guide the development of decisional supports and promote shared decision-making. PROCEDURE: Informed by the Ottawa Decision Support Framework (ODSF), we conducted a qualitative analysis to assess decisional needs and supports reported by AYAs with SCD, their caregivers, and healthcare providers. Semi-structured qualitative interviews were conducted with AYAs and their caregivers, and online crowdsourcing was used with SCD providers. Thematic and descriptive content analyses were used to summarize perspectives on decisional needs and supports regarding disease-modifying therapies. RESULTS: = 14.8 years, 75% physicians) participated. Thematic analysis revealed needs related to: decisional conflict, inadequate knowledge, unclear expectations, and inadequate supports and resources. Six forms of support emerged as important for decision-making: establishing an open and trusting patient/family-provider relationship, providing information, accepting ambivalence and unreadiness, supporting implementation of a decision, addressing inadequate health and social services, and promoting adequate social, emotional, and instrumental help. CONCLUSIONS: This is the first study to assess decisional needs and supports for AYAs with SCD considering disease-modifying therapies. Additional research is needed to examine which decision supports are the most impactful to promote effective shared decision-making in this population.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".