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Record W2146254026 · doi:10.1017/s1041610212001895

Gender differences in health service use for mental health reasons in community dwelling older adults with suicidal ideation

2012· article· en· W2146254026 on OpenAlexafffund
Helen‐Maria Vasiliadis, S. Gagné, Natalia Jozwiak, Michel Préville

Bibliographic record

VenueInternational Psychogeriatrics · 2012
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversité de Sherbrooke
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsSuicidal ideationMental healthMental health servicePsychiatryPsychologyGerontologySuicide preventionClinical psychologyMedicinePoison controlEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: To ascertain gender-specific determinants of antidepressant and mental health (MH) service use associated with suicidal ideation. METHODS: Data used in this study came from the ESA (Enquête sur la Santé des Aînés) survey carried out in 2005-2008 on a large sample of community-dwelling older adults (n = 2,004). Multivariate logistic regression analyses were carried out. RESULTS: The two-year prevalence of suicidal ideation was 8.4% and 20.3% had persistent suicidal thoughts at one-year follow-up. In males, the prevalence of antidepressant and MH service use in respondents with suicidal ideation reached 32.2% and 48.9%, respectively. In females, the corresponding rates were 42.6% and 65.6%. Males were less likely to consult MH services than females when their MH was judged poorly. Male respondents with higher income and education were less likely to use antidepressant and MH services. However, males using benzodiazepines were more likely than females to be dispensed an antidepressant. Among respondents with suicidal ideation, gender was not associated with service use. Younger age, however, was associated with antidepressant use. CONCLUSIONS: Increased promotion campaigns sensitizing men to the prodromal symptoms of depression and the need to foster access to MH care when the disorder is manageable may be needed.

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)0.001
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 score0.025
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.084
GPT teacher head0.406
Teacher spread0.322 · 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

Citations31
Published2012
Admission routes2
Has abstractyes

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