Understanding Contributors of Resilience in Youth With Childhood‐Onset Systemic Lupus Erythematosus Through a Socioecological Lens: A Mixed‐Methods Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify themes contributing to resilience in childhood-onset systemic lupus erythematosus (cSLE), distinguish between profiles of resilience, and examine how they relate to underlying themes and patient characteristics. METHODS: We conducted a mixed-methods study of 21 patients with cSLE aged 11 to 19 years at a Canadian tertiary care center from October 2022 to July 2024. We purposively sampled patients belonging to ethnically and culturally diverse backgrounds to complete semistructured interviews. We qualitatively defined features of resilience and distinguished profiles of low versus high socioecological resilience according to patient median on the Child and Youth Resilience Measure-Revised (CYRM-R). Profiles were then related to sociodemographic (eg, adverse childhood experiences, health literacy), disease features (eg, age at diagnosis, disease duration), and patient-reported outcomes (eg, anxiety and depressive symptoms). RESULTS: Factors contributing to resilience were grouped into five themes: familial environment, social support beyond family, health services and information, life with SLE, and sense of self. Cultural influences were reported to impact several themes. Patients with high resilience (scores above 73 on CYRM-R) reported more facilitators in each thematic area, whereas patients with low resilience experienced more challenges in these areas, in addition to greater number of adverse childhood experiences, lower health literacy, earlier age at diagnosis, longer disease duration and poorer mental health. CONCLUSION: Findings support a dynamic model of resilience, shaped by a combination of sociodemographic, disease, personal, cultural and social factors. This improved understanding of resilience may help direct comprehensive care for youth with cSLE and guide targeted interventions for youth at risk of poor outcomes.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".