Resilience processes among Ukrainian youth preparing to build resilience with peers during the Ukraine-Russia war
Bibliographic record
Abstract
The war in Ukraine significantly impacts the mental health and well-being of its youth. Like other communities affected by war, Ukraine’s youth are at risk of developing psychopathological symptoms, and there is a shortage of mental health and psychosocial support services to address this. Resilience-building initiatives present an alternative approach to supporting the well-being of young people by promoting protective processes to enhance the likelihood of positive development in the context of adversity. Emerging research findings suggest that young people themselves can serve as powerful facilitators of such initiatives with one another. Yet, evidence about culturally and contextually relevant protective processes is needed to guide such interventions, especially among young people experiencing the war and working to boost resilience within their communities. In this study, we identified key protective processes Ukrainian youth depend on as they adapt to the conflict while also preparing to implement a resilience-building intervention as a facilitator. Through thematic analysis of transcripts of three training sessions with Ukrainian youth (n = 15, 100% female; aged 18–22), we identified the following themes: positive thinking, sense of control, emotion awareness and regulation, close personal relationships, and community support. Findings also highlighted the cultural and contextual nuance of these protective processes, as well as individual differences in the ways they co-occurred and manifested within each youth. Results have implications for developing tailored yet flexible resilience-building interventions that can be delivered by lay people, including youth with their peers, in Ukraine and other cultures and contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".