Social Capital Transformation, Voluntarily Services and Mental Health During Times of Military Conflict in Ukraine
Bibliographic record
Abstract
BACKGROUND: The effects of war as well as military conflict include long-term physical and psychological harm to children and adults. Social relations and trust play a role in peace building and conflict resolution. Social capital is believed to facilitate institutional and interpersonal trust as well as safety and security, and thus may become an important resource in times of military conflict.OBJECTIVE: The aims of this study are to analyse how social capital may be transformed due to a military conflict in contemporary Ukraine and to explore the role of voluntarily services in this change. Further we aim to discuss the possible influence of social capital transformation on mental health in times of military conflict.METHODS: A qualitative case study design was chosen to explore it. In-depth interviews were chosen as a method for data collection. Informant’s selection criteria were: either to be involved in volunteering activities in the city of Khmelnitsky (which is the place of research) or to receive volunteering help. 18 interviews were conducted.Informants were reached by snowball sampling. Interviews are collected, transcribed, translated and analyzed using constructive Grounded Theory approach of Charmaz.RESULTS: Our results show that social capital transforms during military conflict experiences. The changes happen both in cognitive and structural components since they are connected. The most important changes occur in bonding social capital, where new formation such as brotherhood, emerges and replaces previous bonding ties with family and friends. In addition, voluntarily acting actors (those who normally belong to bridging social capital) transform into relations with bonding entities. New forms of social capital are thus generated through the existence of voluntary services, and these networks provide essential social support in times of military conflict. Perceived support softens negative emotional responses to traumatic events. In line with the stress-buffering model, our results support that the formation of new social capital in times of military conflict may protect against the negative mental health effects of these experiences.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".