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Record W6908724811 · doi:10.26181/22230070

The War Trauma and Mental Health Challenges for South Sudanese Men in the Diaspora

2022· article· en· W6908724811 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsSpanish Civil WarRefugeeMental healthSettlement (finance)DiasporaDisplaced personTraumatic stressWorld War IIPublic health

Abstract

fetched live from OpenAlex

Untreated trauma has long-term consequences on people affected by traumatic experiences such as war and displacement. South Sudan is a country affected by a long civil war that left many people affected by trauma and mental health. The impact of trauma and mental health is an unresearched area within the South Sudanese population. This study examined the existing literature about the trauma and mental health issues of the South Sudanese men living in the diaspora. The consequences of post-traumatic stress disorder are very visible among individuals and families of the South Sudanese people living in the diaspora. The hostilities of the civil war did not stop the moment South Sudanese veterans ceased fire and fled their home country; the trauma and painful experiences lived beyond the final days of fighting. As a result of civil war, many families and individuals were displaced to refugee camps, and some migrated to Western countries such as Australia, Canada, New Zealand, the United States, the United Kingdom and others. These families and individuals are still in the settlement phase in Western countries. During the settlement period, research, healthcare, social services and community conversations centre on settlement challenges and visible challenges and struggles, such as socio-economic, employment, language barriers and support, while little attention is given to the invisible effects of war-related trauma, especially among men.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0120.000
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 head0.060
GPT teacher head0.362
Teacher spread0.302 · 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 designQualitative
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

Citations2
Published2022
Admission routes1
Has abstractyes

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