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Record W2347150456 · doi:10.1017/s2045796016000329

Under-diagnosis of mood disorders in Canada

2016· article· en· W2347150456 on OpenAlexafffundabout
L Pelletier, S. O’Donnell, Jennifer Dykxhoorn, Louise McRae, Scott B. Patten

Bibliographic record

VenueEpidemiology and Psychiatric Sciences · 2016
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of CalgaryMental Health Commission of CanadaPublic Health Agency of Canada
FundersAlberta Innovates
KeywordsMood disordersPsychiatryPsychologyMoodMedicinePsychotherapistClinical psychologyAnxiety

Abstract

fetched live from OpenAlex

AIMS: Under-diagnosis of mood disorders occurs worldwide. In this study, we characterized and compared Canadians with symptoms compatible with a mood disorder by diagnosis status; and described the associated health impacts, use of health services and perceived need for care. METHODS: Respondents to the 2012 Canadian Community Health Survey - Mental Health, a nationally representative sample of Canadians age ≥15 years were assessed for symptoms compatible with mood disorders based on a Canadian adaptation of the World Health Organization Composite International Diagnostic Interview (n = 23 504). Descriptive and multivariate regression analyses were performed. RESULTS: In 2012, an estimated 5.4% (1.5 million) Canadians aged 15 years and older reported symptoms compatible with a mood disorder, of which only half reported having been professionally diagnosed. The undiagnosed individuals were more likely to be younger (mean age: 36.2 v. 41.8), to be single (49.5 v. 32.7%), to have less than a post-secondary graduation (49.8 v. 41.1%) and to have no physical co-morbidities (56.4 v. 35.7%), and less likely to be part of the two lower income quintiles (49.6 v. 62.7%) compared with those with a previous diagnosis. Upon controlling for all socio-demographic and health characteristics, the associations with age and marital status disappeared. While those with a previous diagnosis reported significantly greater health impacts and were more likely to have consulted a health professional for their emotional and mental health problems in the previous 12 months compared with those undiagnosed (79.4 v. 31.0%), about a third of both groups reported that their health care needs were only partially met or not met at all. CONCLUSIONS: Mood disorders are prevalent and can profoundly impact the life of those affected, however, their diagnosis remains suboptimal and health care use falls short of apparent needs. Improvements in mental health literacy, help-seeking behaviours and diagnosis are needed. In light of the heterogeneity of mood disorders in terms of symptoms severity, impacts and prognosis, interventions must be tailored accordingly.

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)0.001
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 score0.200
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.062
GPT teacher head0.389
Teacher spread0.327 · 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

Citations59
Published2016
Admission routes3
Has abstractyes

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