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Record W2936132728 · doi:10.1136/lupus-2019-lsm.92

92 Engaging patients and parents to improve mental health for youth with systemic lupus erythematosus

2019· article· en· W2936132728 on OpenAlexaff
Andrea Knight, Oluwatunmise A. Fawole, Michelle Reed, Lauren Faust, Tamar B. Rubinstein, Julia G. Harris, Aimee O. Hersh, Karen Onel, Erica Lawson, Kaveh Ardalan, Esi M. Morgan, Anne Paul, Judith Barlin, Paola Daly, Mitali Dave, Shannon Malloy, Shari Hume, Suzanne Schrandt, Laura Marrow, Angela Chapson, Donna Jo Napoli, Michael Napoli, Miranda Moyer, Rachel Adamski, Vincent Del Gaizo, Martha Rodriguez, Emily von Scheven

Bibliographic record

VenueAbstracts · 2019
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineMental healthAnxietySystemic lupus erythematosusDiseaseRheumatologyFamily medicineIntervention (counseling)PsychiatryClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

<h3>Background</h3> Mental health conditions are common in youth with systemic lupus erythematosus (SLE), yet intervention strategies are understudied. We used a patient-engaged approach to investigate the mental health needs of youth with SLE. <h3>Methods</h3> An anonymous online survey examined beliefs and experiences with mental health for youth with SLE. Eligible youth ages 14–24 years had a diagnosis of SLE and reported specific treatment for the condition. Parents of youth 8–24 years meeting the above criteria were also eligible to participate. The survey was developed in collaboration with patient and parent advisors, the Childhood Arthritis and Rheumatology Research Alliance (CARRA), and the Patients, Advocates, and Rheumatology Teams Network for Research and Service (PARTNERS). Participants were recruited through the Lupus Foundation of America and CARRA clinics. We tabulated youth responses for i) self-reported prevalence of mental health problems, categorized into mutually exclusive clinician-diagnosed disorders and self-diagnosed symptoms, and ii) mean Likert ratings (0=low, 4=high) for the impact of disease related-factors on their mental health. We also compared youth and parent responses using regression models to examine comfort level with potential mental health providers. <h3>Results</h3> 102 respondents included 59 patients (58%) and 43 (42%) parents. Youth had a mean age of 20.9 (standard deviation, SD=3.4) years, and mean disease duration of 6.9 (SD 4.0) years. History of a mental health problem was reported by 21 youth (36%), of which 66% said that their rheumatologist was unaware. Clinician-diagnosed anxiety was reported by 19%, depression by 12%, and adjustment disorders by 19%; another 17%, 8% and 10% had self-reported symptoms of these disorders, respectively. Mean Likert ratings by youth indicated that disease aspects most impacting mental health were worry about disease impact on the future at 3.0 (SD 1.2), worry about having a flare at 2.9 (1.2), and worry about medication side effects at 2.8 (1.3). Youth and parents felt most comfortable discussing mental health concerns with rheumatologists and primary care providers, and least comfortable with social workers and school counselors (figure 1). <h3>Conclusions</h3> Youth with SLE have high rates of diagnosed and undiagnosed mental health problems, which are impacted by their disease. Mental health intervention strategies in rheumatology settings may improve mental health education, screening and treatment for these youth. <h3>Funding Source(s):</h3> The Childhood Arthritis and Rheumatology Research Alliance

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.033
GPT teacher head0.349
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2019
Admission routes1
Has abstractyes

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