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Record W2133334715 · doi:10.1177/070674370505001002

The Canadian Community Health Survey: Mental Health and Well-Being

2005· article· en· W2133334715 on OpenAlexaffvenueabout
Ronald Gravel, Yves Béland

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsMental healthData collectionInterviewCommunity healthPsychologyNational Health Interview SurveyGerontologyMental illnessSample (material)MedicinePsychiatryEnvironmental healthPublic healthNursingSociologySocial sciencePopulation

Abstract

fetched live from OpenAlex

As part of the Canadian Community Health Survey (CCHS) biennial strategy, the provincial survey component of the first CCHS cycle (Cycle 1.2) focused on different aspects of the mental health and well-being of Canadians living in private dwellings. Moreover, the survey collected data on prevalences of specific mental disorders and problems, use of mental health services, and economic and personal costs of having a mental illness. Data collection began in May 2002 and extended over 8 months. More than 85% of all interviews were conducted face-to-face and used a computer-assisted application. The survey obtained a national response rate of 77%. This paper describes several key aspects of the questionnaire content, the sample design, interviewer training, and data collection procedures. A brief overview of the CCHS regional component (Cycle 1.1) is also given.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.015
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.028
GPT teacher head0.334
Teacher spread0.306 · 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

Citations307
Published2005
Admission routes3
Has abstractyes

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