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Record W2093316541 · doi:10.4236/health.2013.510212

Gender differences in mental health service utilization among respondents reporting depression in a national health survey

2013· article· en· W2093316541 on OpenAlexafffundabout
Katherine L. Smith, Flora I. Matheson, Rahim Moineddin, James R. Dunn, Hong Lu, John Cairney, Richard H. Glazier

Bibliographic record

VenueHealth · 2013
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsInstitute for Clinical Evaluative SciencesPublic Health OntarioMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
FundersSocial Sciences and Humanities Research Council of CanadaInstitute for Clinical Evaluative SciencesOntario Ministry of Health and Long-Term CareMcMaster University
KeywordsMental healthDepression (economics)PsychiatryMedicineMental health servicePsychology

Abstract

fetched live from OpenAlex

This study examined whether people who self-reported depression sought mental health treatment in the year after being interviewed, and how gender affected utilization. Depression data were obtained from the Canadian Community Health Survey (2000-01), and linked to medical records in Ontario (n = 24,677). Overall, women had higher rates of mental health service utilization, but there were no gender differences in rates of specialist care. The gender difference in mental health contact was greater for those without depression, as opposed to those with depression. Among those without depression, women were significantly more likely than men to use mental health services; however, rates were similar for women and men with depression. This finding suggests that men may be more likely than women to delay seeing a doctor for minor mental health concerns, but will seek help once a problem reaches a threshold.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.140
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.319
GPT teacher head0.492
Teacher spread0.174 · 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

Citations67
Published2013
Admission routes3
Has abstractyes

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