The association between post-traumatic stress-related symptoms, resilience, current stress and past exposure to violence: a cross sectional study of the survival of Quechua women in the aftermath of the Peruvian armed conflict
Bibliographic record
Abstract
BACKGROUND: The long lasting resilience of individuals and communities affected by mass violence has not been given equal prominence as their suffering. This has often led to psychosocial interventions in post-conflict zones being unresponsive to local realities and ill-equipped to foster local strengths. Responding to the renewed interest in resilience in the field of violence and health, this study examines the resilience and post-traumatic responses of Indigenous Quechua women in the aftermath of the political violence in Peru (1980-2000). METHODS: A cross-sectional study examined the relationship between resilience, post-traumatic responses, exposure to violence during the conflict and current life stress on 151 Quechua women participants. Purposive and convenience sampling strategies were used for recruitment in Ayacucho, the area most exposed to violence. The study instruments were translated to Quechua and Spanish and cross-culturally validated. Data was analyzed using hierarchical regression analysis. A locally informed trauma questionnaire of local idioms of distress was also included in the analysis. FINDINGS: Sixty percent of women (n = 91) were recruited from Ayacucho city and the rest from three rural villages; the mean age was 45 years old. Despite high levels of exposure to violence, only 9.3% of the sample presented a level of symptoms that indicated possible PTSD. Resilience did not contribute to the overall variance of post-traumatic stress related symptoms, which was predicted by past exposure to violence, current life stress, age, and schooling (R2 = .421). Resilience contributed instead to the variance of avoidance symptoms (Stand β = -.198, t = -2.595, p = 0.010) while not for re-experiencing or arousal symptoms. CONCLUSIONS: These findings identified some of the pathways in which resilience and post-traumatic responses interrelate in the aftermath of violence; yet, they also point to the complexity of their relationship, which is not fully explained by linear associations, requiring further examination. Age and gender-sensitive health care is considered critical almost fifteen years after the end of the conflict. The notable resilience of Quechua women-despite exposure to a continuum of violence and social inequalities-also calls for enhanced recognition of women not only as victims of violence but also as complex social actors in the reconstruction of post-conflict societies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".