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Record W2064144676 · doi:10.7205/milmed-d-11-00185

Former Combatants in Liberia: The Burden of Possible Traumatic Brain Injury Among Demobilized Combatants

2012· article· en· W2064144676 on OpenAlexaff
Kirsten Johnson, Jana Asher, Michael Kisielewski, Lynn Lieberman Lawry

Bibliographic record

VenueMilitary Medicine · 2012
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsSuicidal ideationPsychiatryDemobilizationMedicineMental healthDisarmamentTraumatic stressPoison controlPopulationSuicide preventionMajor depressive disorderEnvironmental healthMood

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide a better understanding of any associations between Disarmament, Demobilization, and Reintegration, previous head injury, and mental health symptoms among former combatants in Liberia. METHODS: A cluster-sampled national survey of the adult household-based Liberian population. FINDINGS: Former combatants with reported head injury were more likely to experience major depressive disorder symptoms, suicidal ideation and attempts, and current substance abuse. Former combatants with head injury are 2.83 times more likely to have major depressive disorder symptoms, and those with suspected traumatic brain injury are five times more likely to have post-traumatic stress disorder. INTERPRETATION: The poor mental health of former combatants in Liberia, both child and adult, might be mitigated if Disarmament, Demobilization, and Reintegration programming assessed participants for head trauma and traumatic brain injury using simple screening methods. The specific health and mental health needs of ex-combatants--a highly vulnerable group--will need to be addressed by Liberia. If left untreated, ex-combatants with high rates of suicidal ideation and post-traumatic stress disorder might be susceptible to re-recruitment into new conflicts in the region.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.353
Teacher spread0.314 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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