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Record W3131085516 · doi:10.1186/s12969-021-00503-7

Engaging patients and parents to improve mental health intervention for youth with rheumatological disease

2021· article· en· W3131085516 on OpenAlexaff
Oluwatunmise A. Fawole, Michelle V. Reed, Julia G. Harris, Aimee O. Hersh, Martha Rodriguez, Karen Onel, Erica Lawson, Tamar B. Rubinstein, Kaveh Ardalan, Esi M. Morgan, Anne Paul, Judy Barlin, R. Paola Daly, Mitali Dave, Shannon Malloy, Shari Hume, Suzanne Schrandt, Laura Marrow, Angela Chapson, Donna Jo Napoli, Michael Napoli, Miranda Moyer, Vincent Del Gaizo, Ashley Danguecan, Emily von Scheven, Andrea Knight

Bibliographic record

VenuePediatric Rheumatology · 2021
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCure JM FoundationRheumatology Research FoundationLupus Foundation of AmericaArthritis Foundation
KeywordsMedicineMental healthAnxietyDiseaseIntervention (counseling)PopulationPsychiatryDepression (economics)Family medicineClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Mental health disorders are common in youth with rheumatological disease yet optimal intervention strategies are understudied in this population. We examined patient and parent perspectives on mental health intervention for youth with rheumatological disease. METHODS: We conducted a mixed methods cross-sectional study, via anonymous online survey, developed by researchers together with patient/parent partners, to quantitatively and qualitatively examine youth experiences with mental health services and resources in North America. Patients ages 14-24 years with juvenile idiopathic arthritis, juvenile dermatomyositis, or systemic lupus erythematous, and parents of patients ages 8-24 with these diseases were eligible (not required to participate in pairs). Participants self-reported mental health problems (categorized into clinician-diagnosed disorders vs self-diagnosed symptoms) and treatments (e.g. therapy, medications) received for the youth. Multivariate linear regression models compared patient and parent mean Likert ratings for level of: i) comfort with mental health providers, and ii) barriers to seeking mental health services, adjusting for potential confounders (patient age, gender, disease duration, and patient/parent visual analog score for disease-related health). Participants indicated usefulness of mental health resources; text responses describing these experiences were analyzed by qualitative description. RESULTS: Participants included 123 patients and 324 parents. Patients reported clinician-diagnosed anxiety (39%) and depression (35%); another 27 and 18% endorsed self-diagnosed symptoms of these disorders, respectively. 80% of patients with clinician-diagnosed disorders reported receiving treatment, while 11% of those with self-diagnosed symptoms reported any treatment. Patients were less comfortable than parents with all mental health providers. The top two barriers to treatment for patients and parents were concerns about mental health providers not understanding the rheumatological disease, and inadequate insurance coverage. Over 60% had used patient mental health resources, and over 60% of these participants found them to be helpful, although text responses identified a desire for resources tailored to patients with rheumatological disease. CONCLUSION: Self-reported mental health problems are prevalent for youth in this sample with rheumatological disease, and obstacles to mental health treatment include disease-related and logistic factors. Strategies are needed to improve acceptance and accessibility of mental health intervention, including routine mental health screening and availability of disease-specific mental health resources.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
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 science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.295
Teacher spread0.279 · 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 designQualitative
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".

Quick stats

Citations39
Published2021
Admission routes1
Has abstractyes

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