Identifying Targets for Improving Mental Healthcare of Adolescents with Systemic Lupus Erythematosus: Perspectives from Pediatric Rheumatology Clinicians in the United States and Canada
Bibliographic record
Abstract
OBJECTIVE: To identify targets for improving mental healthcare of adolescents with systemic lupus erythematosus (SLE) by assessing current practices and perceived barriers for mental health intervention by pediatric rheumatology clinicians. METHODS: Members of the Childhood Arthritis and Rheumatology Research Alliance (CARRA) completed a Web-based survey assessing current mental health practices, beliefs, and barriers. We examined associations between provider characteristics and the frequency of barriers to mental health screening and treatment using multivariable linear regression. RESULTS: Of the 375 eligible CARRA members, 130 responded (35%) and 119 completed the survey. Fifty-two percent described identification of depression/anxiety in adolescents with SLE at their practice as inadequate, and 45% described treatment as inadequate. Seventy-seven percent stated that routine screening for depression/anxiety in pediatric rheumatology should be conducted, but only 2% routinely used a standardized instrument. Limited staff resources and time were the most frequent barriers to screening. Respondents with formal postgraduate mental health training, experience treating young adults, and practicing at sites with very accessible mental health staff, in urban locations, and in Canada reported fewer barriers to screening. Long waitlists and limited availability of mental health providers were the most frequent barriers to treatment. Male clinicians and those practicing in the Midwest and Canada reported fewer barriers to treatment. CONCLUSION: Pediatric rheumatology clinicians perceive a need for improved mental healthcare of adolescents with SLE. Potential strategies to overcome barriers include enhanced mental health training for pediatric rheumatologists, standardized rheumatology-based mental health practices, and better integration of medical and mental health services.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".