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Record W3006046384 · doi:10.1111/sltb.12624

Psychache Predicts Suicide Attempter Status Change in Students Starting University

2020· article· en· W3006046384 on OpenAlexaff
Christine Lambert, Talia Troister, Zeinab Ramadan, Vanessa Montemarano, G. Cynthia Fekken, Ronald R. Holden

Bibliographic record

VenueSuicide and Life-Threatening Behavior · 2020
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsSuicide preventionPoison controlSuicidal ideationInjury preventionHuman factors and ergonomicsDepression (economics)PsychologyOccupational safety and healthClinical psychologyMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVES: Unlike many investigations that focus on suicide ideation rather than suicidal behavior, the present research evaluates the merit and relative efficacy of psychache (i.e., unbearable mental pain) for predicting self-reported suicide attempts among university students who are starting university. METHOD: A sample of 516 elevated-risk undergraduates was assessed during the first three weeks of starting university and, again, 10 weeks later. RESULTS: Psychache and depression, but not hopelessness, could predict change in suicide attempter status. When measures of psychache, depression, and hopelessness were considered simultaneously, only psychache provided significant, unique predictive power. CONCLUSIONS: Findings are interpreted as supporting Shneidman's model whereby psychache is seen as the cause of 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)0.000
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.122
GPT teacher head0.360
Teacher spread0.238 · 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

Citations42
Published2020
Admission routes1
Has abstractyes

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