Social support of veterans and their families: models of rehabilitation and integration
Bibliographic record
Abstract
The purpose of the study is a comprehensive study of modern models of social support for veterans and their family members in Ukraine. The research process used an interdisciplinary approach that combines elements of sociological, legal, psychological and economic analysis. The comparative legal method was used to analyze national and foreign models of social rehabilitation of veterans; the systemic and structural method was used to identify relationships between levels of social support (state, regional, public); content analysis of regulatory legal acts. The article substantiates that social support for veterans is a key element of national security and social stability, and its effectiveness is determined by the level of interaction between state structures, communities and non-governmental organizations. It was determined that the social adaptation of veterans has a two-stage nature – psychological and socio-economic, which interact in the process of returning to civilian life. The factors of successful adaptation (family support, community, professional rehabilitation, veteran communities) and barriers (stigmatization, loss of identity, limited social infrastructure) were analyzed. It was established that the most promising is an integrated model of social rehabilitation, which combines psychological, professional, educational and economic support. The positive practices of the USA, Canada, Israel and Scandinavian countries, where the assistance system is based on the principles of partnership, multisectorality and a personalized approach, are characterized. The results of the study can be used to improve the state policy of Ukraine in the field of social support for veterans, develop local rehabilitation programs, create comprehensive assistance centers and implement retraining programs and veteran entrepreneurship.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".