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Record W2887342729 · doi:10.1016/j.msard.2018.07.043

Factors associated with perceived need for mental health care in multiple sclerosis

2018· article· en· W2887342729 on OpenAlexafffund
Justine Orr, Çharles N. Bernstein, Lesley A. Graff, Scott B. Patten, James M. Bolton, Jitender Sareen, James Marriott, John D. Fisk, Ruth Ann Marrie

Bibliographic record

VenueMultiple Sclerosis and Related Disorders · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsManitoba HealthNova Scotia Health AuthorityDalhousie UniversityUniversity of CalgaryUniversity of Manitoba
FundersCanadian Institutes of Health ResearchCrohn's and Colitis Canada
KeywordsMultiple sclerosisMedicineMental healthPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Within the multiple sclerosis (MS) population, depression and anxiety are highly prevalent comorbidities that are associated with adverse outcomes such as diminished quality of life and disability progression. In the general population, many people who do not meet formal diagnostic criteria for depression or anxiety disorders still identify a need for mental health care. Limited data are available regarding the perceived need for mental health care among persons with MS. OBJECTIVE: We aimed to determine factors associated with a perceived need for mental health care in the MS population. METHODS: Participants with MS completed the Hospital Anxiety and Depression Scale (HADS) to assess severity of depression and anxiety symptoms, and reported whether they perceived a need for mental health care, in the context of a larger study examining the burden of psychiatric disorders in immune-mediated inflammatory disease. Participants were also evaluated using the Structured Clinical Interview for DSM-IV-TR (SCID) to diagnose depression or anxiety disorders. Participants reported their sociodemographic characteristics, and underwent physical assessments to determine their disability status. Descriptive analyses and binary logistic regression models were used to determine sociodemographic and clinical factors associated with perceived need for mental health care. RESULTS: Of 255 participants enrolled, 251 were included in this analysis. Most participants were women, Caucasian, with post-secondary education, with a mean (SD) age at enrollment of 50.9 (12.9) years. They predominantly had a relapsing-remitting MS course. Nearly one-quarter of participants had a current SCID diagnosis of depression or anxiety (n = 57, 22.7%). Overall, 31.8% (n = 80) of participants reported a need for mental health care. These individuals were slightly younger at enrollment (p = 0.037), but otherwise did not differ with respect to sociodemographic characteristics, compared to participants not reporting this need. Those identifying need for mental health care also had an earlier age of MS symptom onset (p = 0.011). After adjusting for sociodemographic and clinical factors, elevated symptoms of depression (odds ratio [OR] 2.36; 95%CI: 1.06, 5.25) and anxiety (OR 6.08; 95%CI: 2.78, 13.3) were associated with an increased likelihood of reporting a need for mental health care. Any current SCID diagnosis of depression or anxiety was not associated with perceived need for mental health care after accounting for symptoms of depression and anxiety. CONCLUSIONS: One-third of people with MS identified a need for mental health care. Symptoms of anxiety and depression, but not current diagnosed mental health disorders, were the predominant factors associated with a perceived need for care.

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)0.013
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.076
GPT teacher head0.292
Teacher spread0.216 · 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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Citations15
Published2018
Admission routes2
Has abstractno

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