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Record W2138395214 · doi:10.24095/hpcdp.28.4.04

Association of comorbid mood disorders and chronic illness with disability and quality of life in Ontario, Canada

2008· article· en· W2138395214 on OpenAlexaffvenueabout
Tahany M. Gadalla

Bibliographic record

VenueChronic diseases in Canada · 2008
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineMood disordersMoodPsychiatrySuicidal ideationQuality of life (healthcare)Affect (linguistics)ComorbidityFibromyalgiaPoison controlAnxietyInjury preventionEnvironmental healthPsychology

Abstract

fetched live from OpenAlex

Mood disorders are more prevalent in individuals with chronic physical illness compared to individuals with no such illness. These disorders amplify the disability associated with the physical condition and adversely affect its course, thus contributing to occupational impairment, disruption in interpersonal and family relationships, poor health and suicide. This study used data collected in the Canadian Community Health Survey, cycle 3.1 (2005) to examine factors associated with comorbid mood disorders and to assess their association with the quality of life of individuals living in Ontario. Results indicate that individuals with chronic fatigue syndrome, fibromyalgia, bowel disorder or stomach or intestinal ulcers had the highest rates of mood disorders. The odds of having a comorbid mood disorder were higher among women, the single, those living in poverty, the Canadian born and those between 30 and 69 years of age. The presence of comorbid mood disorders was significantly associated with short-term disability, requiring help with instrumental daily activities and suicidal ideation. Health care providers are urged to proactively screen chronically ill patients for mood disorders, particularly among the subgroups found to have elevated risk for these 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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

Citations41
Published2008
Admission routes3
Has abstractyes

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