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Record W2069591881 · doi:10.1097/nmd.0b013e3181c299c2

Correlates of Self-Medication for Anxiety Disorders

2009· article· en· W2069591881 on OpenAlexafffund
Jennifer A. Robinson, Jitender Sareen, Brian J. Cox, James M. Bolton

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2009
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsAnxietySelf-medicationPsychiatryClinical psychologyMental healthQuality of life (healthcare)MedicineConfoundingPsychologyPsychotherapistInternal medicine

Abstract

fetched live from OpenAlex

Self-medication is a common behavior among individuals with anxiety disorders, yet few studies have examined the correlates of this behavior. The current study addresses this issue by exploring the pattern of mental health service use and quality of life among people who self-medicate for anxiety. Data came from the National Epidemiologic Survey on Alcohol and Related Conditions and was limited to the subsample of individuals meeting criteria for an anxiety disorder in the past 12 months (n = 4880). Multiple regression analyses compared 3 groups-(1) no self-medication, (2) self-medication with alcohol, and (3) self-medication with drugs, on mental health service use and quality of life. After adjusting for potentially confounding covariates, individuals who engaged in self-medication had significantly higher service use compared with people with anxiety disorders who did not self-medicate (adjusted odds ratio = 1.41, 95% CI = 1.06-1.89). Self-medication was also associated with a lower mental health-related quality of life compared with those who did not self-medicate. Clinicians should recognize and respond to the unique needs of this particular subpopulation of individuals with anxiety disorders.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.308
Teacher spread0.295 · 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 teacher head, 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".

Quick stats

Citations43
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Nervous and Mental DiseaseSame topicAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesFrench-language works237,207