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Record W2030908562 · doi:10.1177/0886260514534774

Symptoms of Post-Traumatic Stress Disorder Among Battered Women in Lebanon

2014· article· en· W2030908562 on OpenAlexaff
Christelle Khadra, Nancy Wehbe, Jacinthe Lachance Fiola, Wadih Skaff, Mona SAOUMA NEHMÉ

Bibliographic record

VenueJournal of Interpersonal Violence · 2014
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTraumatic stressPoison controlInjury preventionOccupational safety and healthSuicide preventionHuman factors and ergonomicsMedicinePsychiatryDomestic violenceClinical psychologyPsychologyMedical emergency

Abstract

fetched live from OpenAlex

Intimate partner violence against women is common in Lebanon and can lead to major health problems. However, the incidence of symptoms of post-traumatic stress disorder (PTSD) in battered women has not been extensively explored in the Lebanese cultural context. The objectives of this study were as follows: (a) to determine the prevalence of PTSD symptoms among women in Lebanon who have been physically abused by their partners, (b) to assess whether the rate of PTSD symptoms varied according to sociodemographic variables, and (c) to reveal other attributes that might be risk factors for developing symptoms of PTSD. Of the 95 physically abused women who met inclusion criteria, 85 completed a questionnaire including sociodemographic questions, the physical abuse subscale of the Composite Abuse Scale (CAS), and the PTSD Checklist-Civilian Version (PCL-C). Results showed a high prevalence of PTSD symptoms (97%), positively correlated with physical violence (r = .719). Lower education level and recent abuse were correlated with symptom severity, as were the number of problematic habitual behaviors in the abusive partner and the use of psychotherapy. Increased involvement of health care professionals in the detection of women at risk, with referral to appropriate resources, is suggested to improve prevention and management efforts.

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)0.001
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 score0.129
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.284
Teacher spread0.275 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

Explore more

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