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Record W2088754784 · doi:10.1111/sltb.12127

Trajectories of Suicide Ideation, Nonsuicidal Self‐Injury, and Suicide Attempts in a Nonclinical Sample of Military Personnel and Veterans

2014· article· en· W2088754784 on OpenAlexaff
Craig J. Bryan, AnnaBelle O. Bryan, Alexis M. May, E. David Klonsky

Bibliographic record

VenueSuicide and Life-Threatening Behavior · 2014
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSuicide preventionPoison controlSuicidal ideationSuicide attemptInjury preventionHuman factors and ergonomicsOccupational safety and healthPsychologyPsychiatryClinical psychologySuicide ideationMedicineMedical emergency

Abstract

fetched live from OpenAlex

Nonsuicidal self-injury (NSSI) is a risk factor for suicide attempts, but little is known about NSSI among military personnel and veterans, or about the temporal sequencing of NSSI relative to suicide ideation and attempts. This study evaluates trajectories of suicide ideation, NSSI, and suicide attempts in a sample of 422 military personnel and veterans. Of those with a history of NSSI, 77% also experienced suicide ideation. Suicide ideation emerged before NSSI (67%) more often than the reverse (17%). Of those with a history of suicide attempt, 41% also engaged in NSSI. NSSI emerged prior to the first suicide attempt (91%) more often than the reverse (9%). The length of time from suicide ideation to suicide attempt was longer for those who first engaged in NSSI (median = 3.5 years) compared with those who did not engage in NSSI (median = 0.0 years), Wald χ(2)(1) = 11.985, p = .002. Age of onset was earlier for participants reporting NSSI only compared with those reporting both NSSI and suicide attempts (16.71 vs. 22.08 years), F(1, 45) = 4.149, p = .048. NSSI may serve as a "stepping stone" from suicide ideation to attempts for 41% of those who attempt suicide.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.046
GPT teacher head0.343
Teacher spread0.297 · 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.

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

Citations88
Published2014
Admission routes1
Has abstractyes

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