Examining post-conflict stressors in northern Sri Lanka: A qualitative study
Bibliographic record
Abstract
Forcibly displaced individuals typically encounter daily stressors, which can negatively impact mental health above and beyond direct exposure to war-related violence, trauma and loss. Understanding the perspectives of war affected communities regarding daily stressors can enhance the integration of mental health into local primary care. The aim of the current study was to explore how daily stressors are conceptualized in a post-conflict setting. Data collection was completed with 53 adult participants who were recruited from primary healthcare clinics in Northern Province, Sri Lanka. Individual interviews were conducted in Tamil, audio-recorded, translated from Tamil to English, and transcribed. Themes emerging from the data were organized into an analytical framework based on iterative coding and grounded in the daily stressors framework. Stressors were conceptualized as chronic stressors and systemic stressors. Findings indicate that chronic stressors, such as loss of property, permeate daily life and have a profound impact on psychological wellbeing. Interviewees additionally reported that systemic stressors stemmed from unresolved grief for missing family members and limited support from institutions. The results of the current study complement existing literature, suggesting the value of multipronged approaches which identify and address symptoms of complicated bereavement while simultaneously alleviating financial hardship. An understanding of stressors experienced by conflict-affected populations in times of chronic adversity can be informative for the design and implementation of culturally-tailored interventions.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".