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Record W2411095996

Perceived need for mental health care in Canada: Results from the 2012 Canadian Community Health Survey-Mental Health.

2013· article· en· W2411095996 on OpenAlexaffabout
Adam Sunderland, Leanne Findlay

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsMental healthMedicineNeeds assessmentPopulationDistressPsychologyFamily medicineGerontologyPsychiatryEnvironmental healthClinical psychology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Past research and national survey data on Canadians' perceived need for mental health care (MHC) have focused on unmet needs overall, and have not considered specific types of MHC needs or the extent to which needs are met. DATA AND METHODS: Using data from the 2012 Canadian Community Health Survey-Mental Health, this article describes the prevalence of perceived MHC needs for information, medication, counselling and other services. The degree to which each type of need was met is explored. Associations between risk factors for having MHC needs and the extent to which needs were met are investigated. RESULTS: In 2012, an estimated 17% of the population aged 15 or older reported having had an MHC need in the past 12 months. Two-thirds (67%) reported that their need was met; for another 21%, the need was partially met; and for 12%, the need was unmet. The most commonly reported need was for counselling, which was also the least likely to be met. Distress was identified as a predictor of perceived MHC need status. INTERPRETATION: Many Canadians are estimated to have MHC needs, particularly for counselling. People with elevated levels of distress are significantly more likely to have unmet and partially met MHC needs than to have fully met MHC needs, regardless of the presence of mental or substance 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.326
Teacher spread0.258 · 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.

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

Citations155
Published2013
Admission routes2
Has abstractyes

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