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Record W2791073564 · doi:10.1097/nmd.0000000000000799

Suicide Behavior and Chronic Pain

2018· article· en· W2791073564 on OpenAlexaboutno aff
Margaret Legarreta, Elliott Bueler, Jennifer DiMuzio, Erin McGlade, Deborah Yurgelun‐Todd

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMcGill Pain QuestionnairePain catastrophizingChronic painClinical psychologySuicidal ideationPsychologyPsychiatryPerspective (graphical)Rating scaleHuman factors and ergonomicsPhysical therapyPoison controlMedicineVisual analogue scaleDevelopmental psychologyMedical emergency

Abstract

fetched live from OpenAlex

This study examined differences in suicidal ideation (SI) and suicide attempts (SAs) among veterans with chronic pain. Pain-specific variables, including catastrophic thinking, disability, and sensory, affective, and evaluative pain descriptors, were a focus. Structured diagnostic and clinical interviews were conducted to examine SI/SA and mental health. Veterans completed the Structured Clinical Interview for DSM-IV and the Columbia-Suicide Severity Rating Scale to assess Axis I symptoms and suicidal behavior(s). Self-report questionnaires were used to evaluate the participants' subjective experience of chronic pain, which included the McGill Pain Questionnaire, Pain Catastrophizing Scale, and Pain Disability Index. The findings add to previous literature by suggesting pain-related catastrophic thinking specifically is related to elevated risk for SA, whereas affective and sensory pain are associated with SI. The study results support the need to assess pain from a multifaceted perspective and to examine the different experiences of pain, such as sensory and affective constructs, when discussing suicide risk in veterans.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.323
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 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

Citations14
Published2018
Admission routes1
Has abstractyes

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