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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».