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Record W2624941449 · doi:10.1177/0706743717714467

Service Use and Unmet Needs for Substance Use and Mental Disorders in Canada

2017· article· en· W2624941449 on OpenAlexaffvenueabout
Karen Urbanoski, Dakota Inglis, Scott Veldhuizen

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMcMaster UniversityUniversity of Victoria
Fundersnot available
KeywordsMental healthMedicinePopulationPsychiatrySubstance useAnxietySubstance abuseHealth careMood disordersGerontologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate patterns and predictors of help seeking and met/unmet needs for mental health care in a national population health survey. METHOD: Participants were respondents to the 2012 Canadian Community Health Survey on Mental Health (CCHS-MH; n = 25,133). We used regression to identify the diagnostic and sociodemographic predictors of the use of informal supports, primary care, and specialist care, as well as perceived unmet needs. RESULTS: Eleven percent of Canadians reported using professionally led services for mental health or substance use in 2012, while another 9% received informal supports. Two-thirds of people with substance use disorders did not receive any care, and among those who did, informal supports were most common. Seventy-four percent of people with mood/anxiety disorders and 88% of those with co-occurring disorders did access services, most commonly specialist mental health care. Men, older people, members of ethnocultural minorities, those not born in Canada, those with lower education, and those with higher incomes were less likely to receive care. Unmet needs were higher among people with substance use disorders. CONCLUSIONS: Gaps in services continue to exist for some potentially vulnerable population subgroups. Policy and practice solutions are needed to address these unmet needs. In particular, the convergence of research pointing to gaps in the availability and accessibility of high-quality services for substance use in Canada demands attention.

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 score0.079
Threshold uncertainty score0.401

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 head0.025
GPT teacher head0.249
Teacher spread0.224 · 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

Citations147
Published2017
Admission routes3
Has abstractyes

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