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Record W4385766301 · doi:10.2196/51324

The Effects of Suicide Exposure on Mental Health Outcomes Among Post-9/11 Veterans: Protocol for an Explanatory, Sequential, Mixed Methods Study

2023· article· en· W4385766301 on OpenAlexvenueno aff
Nina A. Sayer, David Nelson, Jaimie L. Gradus, Rebecca K. Sripada, Maureen Murdoch, Alan R. Teo, Robert J. Orazem, Julie Cerel

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
FundersHealth Services Research and DevelopmentU.S. Department of Veterans Affairs
KeywordsMental healthMedicineSuicidal ideationVeterans AffairsPsychiatrySuicide preventionOccupational safety and healthEmergency departmentPoison controlSuicide methodsClinical psychologyMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: The toll associated with suicide goes well beyond the individual who died. This study focuses on a risk factor for veteran suicide that has received little previous empirical attention-exposure to the suicide death of another person. OBJECTIVE: The study's primary objective is to describe the mental health outcomes associated with suicide exposure among veterans who served on active duty after September 2001 ("post-9/11"). The secondary objective is to elucidate why some veterans develop persistent problems following suicide exposure, whereas others do not. METHODS: This is an explanatory, sequential, mixed methods study of a nationally representative sample of post-9/11 veterans enrolled in Department of Veterans Affairs (VA) health care. Our sampling strategy was designed for adequate representation of female and American Indian and Alaska Native veterans to allow for examination of associations between suicide exposure and outcomes within these groups. Primary outcomes comprise mental health problems associated with trauma and loss (posttraumatic stress disorder and prolonged grief disorder) and suicide precursors (suicidal ideation, attempts, and planning). Data collection will be implemented in 3 waves. During wave 1, we will field a brief survey to a national probability sample to assess exposure history (suicide, other sudden death, or neither) and exposure characteristics (eg, closeness with the decedent) among 11,400 respondents. In wave 2, we will include 39.47% (4500/11,400) of the wave-1 respondents, stratified by exposure history (suicide, other sudden death, or neither), to assess health outcomes and other variables of interest. During wave 3, we will conduct interviews with a purposive subsample of 32 respondents exposed to suicide who differ in mental health outcomes. We will supplement the survey and interview data with VA administrative data identifying diagnoses, reported suicide attempts, and health care use. RESULTS: The study began on July 1, 2022, and will end on June 30, 2026. This is the only national, population-based study of suicide exposure in veterans and the first one designed to study differences based on sex and race. Comparing those exposed to suicide with those exposed to sudden death for reasons other than suicide (eg, combat) and those unexposed to any sudden death may allow for the identification of the common and unique contribution of suicide exposure to outcomes and help seeking. CONCLUSIONS: Integrating survey, qualitative, and VA administrative data to address significant knowledge gaps regarding the effects of suicide exposure in a national sample will lay the foundation for interventions to address the needs of individuals affected by a suicide death, including female and American Indian and Alaska Native veterans. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51324.

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)0.054
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.065
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.044
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0040.005
Science and technology studies0.0050.002
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0650.010

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 head0.379
GPT teacher head0.649
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations4
Published2023
Admission routes1
Has abstractyes

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