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Record W2975705182

Concurrent mental and substance use disorders in Canada.

2017· article· en· W2975705182 on OpenAlexaffabout
Saeeda Khan

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsMental healthAnxietyPsychiatryMoodSocioeconomic statusPrevalence of mental disordersMood disordersClinical psychologySubstance abuseAnxiety disorderMedicinePsychologyEnvironmental healthPopulation
DOInot available

Abstract

fetched live from OpenAlex

Based on results of the 2012 Canadian Community Health Survey-Mental Health, 1.2% of Canadians aged 15 to 64 (an estimated 282,000) experienced mental and substance use disorders concurrently in the previous year (at least one mood/anxiety disorder and one substance use disorder). Demographic, socioeconomic, health status and service use characteristics of the concurrent disorder group were compared with those of people who had only a mood/ anxiety disorder or only a substance use disorder. Those with concurrent disorders had consistently poorer psychological health and higher use of health services and were more likely to report partially met/unmet needs than the substance use disorder group, even when demographic and socioeconomic factors and number of chronic health conditions were taken into account. Apparent similarities in health status, service use and partially met/unmet needs between the concurrent disorders and mood/anxiety disorder groups did not persist in multivariate analysis. The findings suggest that the complexity of concurrent disorders contributes to poorer psychological health outcomes and higher health service use, compared with having only a mood/anxiety disorder or a substance use disorder.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.032
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.048
GPT teacher head0.296
Teacher spread0.248 · 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

Citations52
Published2017
Admission routes2
Has abstractyes

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