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Record W2977894201 · doi:10.1111/famp.12487

Perceptions of War Trauma and Healing of Marital Relations Among Torture‐surviving Congolese Couples Participating in Multicouple Therapy

2019· article· en· W2977894201 on OpenAlexaff
Erin R. Morgan, Elizabeth Wieling, Jon Hubbard, Lekie Dwanyen

Bibliographic record

VenueFamily Process · 2019
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsCascades (Canada)
Fundersnot available
KeywordsTorturePsychologyPsychological interventionPsychiatryPolitical scienceHuman rightsLaw

Abstract

fetched live from OpenAlex

Citizens of the Democratic Republic of Congo (DRC) experienced widespread torture during national wars between 1998 and 2003. Couples who survived and stayed intact suffered tremendous relationship stress. This study used a critical ethnography framework to explore the prewar, wartime, and postwar experiences of 13 torture-surviving couples who participated in a 10-session Torture-Surviving Couple Group in 2008 in the DRC. The group was designed to address the relational effects of torture and war trauma. Participants reported profound negative effects of the war on their relationships; mostly positive experiences during the group, including marital and peer connection and relationship growth; and a number of improvements in their relationship after the group. Implications include support for the use of relational interventions informed by both treatments for traumatic stress and couple approaches to promote trauma healing. Future directions call for increased funding, research, training, and clinical action to treat the effects of traumatic stress on relational family dynamics.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
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 head0.032
GPT teacher head0.338
Teacher spread0.306 · 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 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

Citations6
Published2019
Admission routes1
Has abstractyes

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