Healthcare and Research Priorities of Adolescents and Young Adults with Systemic Lupus Erythematosus: A Mixed-methods Study
Bibliographic record
Abstract
OBJECTIVE: Managing juvenile-onset systemic lupus erythematosus (SLE) is particularly challenging. The disease may be severe, adolescent patients have complex medical and psychosocial needs, and patients must navigate the transition to adult services. To inform patient-centered care, we aimed to identify the healthcare and research priorities of young patients with SLE and describe the reasons underpinning their priorities. METHODS: Face-to-face, semistructured interviews and focus groups were conducted with patients with SLE, aged from 14 to 26 years, from 5 centers in Australia. For each of the 5 allocation exercises, participants allocated 10 votes to (1) research topics; research questions on (2) medical management, (3) prevention and diagnosis, (4) lifestyle and psychosocial; and (5) healthcare specialties, and discussed the reasons for their choices. Descriptive statistics were calculated for votes and qualitative data were analyzed thematically. RESULTS: The 26 participants prioritized research that alleviated the psychological burden of SLE. They allocated their votes toward medical and mental health specialties in the management of SLE, while fewer votes were given to physiotherapy/occupational therapy and dietetics. The following 7 themes underpinned the participants' priorities: improving service shortfalls, strengthening well-being, ensuring cost efficiency, minimizing family/community burden, severity of comorbidity or complications, reducing lifestyle disruption, and fulfilling future goals. CONCLUSION: Young patients with SLE value comprehensive care with greater coordination among specialties. They prioritized research focused on alleviating poor psychological outcomes. The healthcare and research agenda for patients with SLE should include everyone involved, to ensure that the agenda aligns with patient priorities, needs, and values.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".