Symptoms of Post-Traumatic Stress Disorder Among Battered Women in Lebanon
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".