Young Adults Navigating Life Amid the Pressures of War in Ukraine
Bibliographic record
Abstract
This critical ethnographic study examines the psychological, socio-economic, and cultural impacts of the armed conflict in Ukraine on young adults (aged 18–40), focusing on resilience and coping mechanisms. Using exploratory mixed-methods, the study included a literature review, semi-structured focus group with 17 participants, and a survey of 47 respondents. Findings show that 94% of respondents reported significant stress and anxiety, consistent with trauma models of prolonged conflict. Displacement, job insecurity, and disrupted education compounded socio-economic hardship, while strong family and community networks supported resilience. The conflict’s gendered nature emerged, with young men disproportionately affected by conscription pressures.The research addresses gaps in the literature by exploring how societal perceptions of mental health, displacement policies, educational access, and psychological support shape resilience during armed conflict, areas largely underexamined in Ukraine before 2022. It also situates findings within the broader socio-political and cultural context, contrasting fragmented governmental responses with emerging integrated approaches. By highlighting resilience in the face of systemic and personal challenges, this study underscores the need for targeted interventions in mental health, education, and economic recovery for young adults in conflict zones.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".