The Long-Term Mental Health Consequences of Torture, Loss, and Insecurity: A Qualitative Study Among Survivors of Armed Conflict in the Dang District of Nepal
Bibliographic record
Abstract
Nepal has witnessed several periods of organized violence since its beginnings as a sovereign nation. Most recently, during the decade-long Maoist Conflict (1996-2006), armed forces used excessive violence, including torture, resulting in deaths and disappearances. Moreover, there is widespread gender-, ethnic- and caste-based discrimination, and grossly unequal distribution of wealth in the country. While the immediate mental health effects of the conflict are well studied, less is known about the ways Nepalese survivors perceive their mental health problems, seek help and respond to mental health treatment in the long-term. This research project begins to provide insight into these complexities. Semi-structured interviews were carried out with 25 people (14 men, 11 women) aged 30 to 65 in Dang district in 2013. To elicit illness narratives, a translated and culturally adapted version of the McGill Illness Narrative Interview (MINI) was used. Additionally, participants were interviewed about their war experiences and present-day economic and social situations. The transcripts were coded using deductive and inductive approaches and analyzed through thematic analysis. The study provides insight into temporal narratives of illness experience; salient prototypes regarding current health problems; and explanatory models, including labels, causal attributions, treatment expectations, course, and outcome. It also explores help- and health-seeking behaviour and pathways to care. Symptoms were found to be widespread and varied, and were not solely attributed to violent experiences and memories, but also to everyday stressors related to survivors’ economic, social, and familial situations. In order to ease their physical and emotional pain and socioeconomic pressures, participants resorted to coping strategies such as social activities, avoidance, withdrawal, and substance use. Many participants had received biomedical treatment for their psychosocial problems from doctors and specialists working in public and private sector clinics and hospitals as well as different forms of traditional healing. These results shed light on the long-term impact of the Nepalese conflict on survivors of extreme violence, highlighting local explanatory models and help- and health-seeking behaviours. These findings inspire recommendations made for the development of appropriate and holistic psychosocial interventions focusing on well-being, social determinants of health, and human rights.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".