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Record W4391337817 · doi:10.1097/nmd.0000000000001731

Lack of Identity and Suicidality

2024· article· en· W4391337817 on OpenAlexaff
Angela Russolillo, Alicia Spidel, David Kealy

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2024
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of British ColumbiaKwantlen Polytechnic UniversitySimon Fraser UniversityProvidence Health Care
Fundersnot available
KeywordsPsychologyMediationIdentity (music)DistressSuicidal ideationClinical psychologyAssociation (psychology)Mental healthPsychological distressSuicide preventionPsychiatryPoison controlMedicinePsychotherapistMedical emergency

Abstract

fetched live from OpenAlex

ABSTRACT: Identity disturbance has been connected to both psychological distress and suicidality, and associated with emotion dysregulation. However, despite empirical evidence of a relationship between lack of identity and poor psychiatric outcomes, the link between impaired identity and emotion dysregulation in suicide risk remains underexplored, particularly among individuals seeking outpatient mental health services. Using data from a large clinical sample (n = 246), the present study examined the association between lack of identity and suicidality and the role of emotion dysregulation within this process. Findings indicated that the mediation model was significant, with emotion regulation difficulties significantly mediating the association between lack of identity and future suicidal behavior. Furthermore, the indirect effect of lack of identity on anticipated suicidality remained significant beyond general distress and past suicide attempt. Our findings add to the literature examining the complex relationship among lack of identity, emotion regulation, and suicidality.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.385
Teacher spread0.318 · 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 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
Published2024
Admission routes1
Has abstractyes

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