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Record W2106006518 · doi:10.1176/foc.7.1.foc106

Suicide and Its Prevention Among Older Adults

2009· article· en· W2106006518 on OpenAlexaffabout
Marnin J. Heisel

Bibliographic record

VenueFOCUS The Journal of Lifelong Learning in Psychiatry · 2009
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsPsycINFOSuicidal ideationSuicide preventionMedicinePsychological interventionRandomized controlled trialPoison controlPsychiatryMental healthOutreachInjury preventionMEDLINEGerontologyPsychologyMedical emergency

Abstract

fetched live from OpenAlex

Objective: To review the research on the epidemiology, risk and resiliency, assessment, treatment, and prevention of late-life suicide. Method: I reviewed mortality statistics. I searched MEDLINE and PsycINFO databases for research on suicide risk and resiliency and for randomized controlled trials with suicidal outcomes. I also reviewed mental health outreach and suicide prevention initiatives. Results: Approximately 12/100,000 individuals aged 65 years or over die by suicide in Canada annually. Suicide is most prevalent among older white men; risk is associated with suicidal ideation or behaviour, mental illness, personality vulnerability, medical illness, losses and poor social supports, functional impairment, and low resiliency. Novel measures to assess late-life suicide features are under development. Few randomized treatment trials exist with at-risk older adults. Conclusions: Research is needed on risk and resiliency and clinical assessment and interventions for at-risk older adults. Collaborative outreach strategies might aid suicide prevention. (Reprinted with permission from Canadian Journal of Psychiatry 2006;51:143–154. https://ww1.cpa-apc.org/Publications/Archives/CJP/2006/march1/heisel-IR.asp )

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 score0.028
Threshold uncertainty score0.503

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 integrity0.0000.001
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 head0.013
GPT teacher head0.300
Teacher spread0.287 · 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

Citations1
Published2009
Admission routes2
Has abstractyes

Explore more

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