Social Work for Servicewomen in Ukraine: A Qualitative Inquiry into Social Work Practices in Private Healthcare Facilities
Bibliographic record
Abstract
Introduction: Given the growing participation of women in the Armed Forces of Ukraine and the gender-specific needs they face after demobilisation, the author analyses the importance of implementing gender-sensitive, multidisciplinary, and individualised support. The aim of this study is to examine the role of social work in supporting servicewomen, using the example of private healthcare institutions in Ukraine, while taking into account gender-specific factors and wartime challenges. Methods: The methodology is based on a qualitative approach, which includes 18 semi-structured in-depth interviews with women servicewomen (n=10), social workers (n=5) and clinic managers (n=3), as well as case studies of three private clinics in Kyiv, Lviv, and Dnipro. The study also includes a comparative analysis of support models in Canada, Norway, and the UK. The findings show that 90% of female servicewomen reported severe emotional exhaustion and symptoms of post-traumatic stress disorder after demobilisation; 80% expressed reluctance to seek state support due to stigma and distrust; and 60% faced discrimination in the military. Results: The results demonstrate the effectiveness of approaches such as case management, client-centred programmes, women's mentoring, and mobile multidisciplinary teams. However, several barriers were identified, including limited funding, insufficiently trained personnel, the absence of clear standards, and persistent gender stereotypes. Conclusion: Finally, the article emphasises the importance of further integrating private healthcare facilities into the national veteran support system, the need for specialised professional training, and the development of partnerships between medical institutions, the state, and civil society to ensure the sustainable protection of the rights and well-being of servicewomen.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".