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Record W1608467113 · doi:10.1002/erv.2347

Comorbidity Between Lifetime Eating Problems and Mood and Anxiety Disorders: Results from the Canadian Community Health Survey of Mental Health and Well‐being

2015· article· en· W1608467113 on OpenAlexaffabout
Xiangfei Meng, Carl D’Arcy

Bibliographic record

VenueEuropean Eating Disorders Review · 2015
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of SaskatchewanMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsAnxietyMoodEating disordersComorbidityPsychiatryMood disordersMental healthNational Comorbidity SurveyClinical psychologyPrevalence of mental disordersPsychologyDepression (economics)Psychological interventionPopulationAnxiety disorderMedicineEnvironmental health

Abstract

fetched live from OpenAlex

This study was to examine profiles of eating problems (EPs), mood and anxiety disorders and their comorbidities; explore risk patterns for these disorders; and document differences in health service utilization in a national population. Data were from the Canadian Community Health Survey of Mental Health and Well-being. The lifetime prevalence of EPs was 1.70% among Canadians, compared with 13.25% for mood disorder, 11.27% for anxiety disorder and 20.16% for any mood or anxiety disorder. Almost half of those with EPs also suffered with mood or anxiety disorders. A similar pattern in depressive symptoms was found among individuals with major depression and EPs, but individuals with EPs reported fewer symptoms. Factors associated with the comorbidity of EPs and mood and anxiety disorders were identified. Individuals with EPs reported more unmet needs. Patients with EPs should be concomitantly investigated for mood and anxiety disorders, as similar interventions may be effective for both.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.140
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.336
Teacher spread0.257 · 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".

Quick stats

Citations38
Published2015
Admission routes2
Has abstractyes

Explore more

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