Alone on the frontline: The first report of PTSD prevalence and risk in de-occupied Ukrainian villages
Bibliographic record
Abstract
Importance: The ongoing Russian invasion of Ukraine marks a critical juncture in a series of events posing severe threat to the health of Ukrainian citizens. While recent reports reveal higher rates of PTSD in Ukrainian refugees following Russia’s invasion – data for Ukrainians remaining at the warfront is inherently difficult to access. A primarily elderly demographic, Ukrainians in previously Russian-occupied areas near the front (UPROANF) are at particular risk. Design: Data was sourced from screening questionnaires administered between March 2022 and July 2023 by mobile health clinics providing services to UPROANF. Setting: Previously occupied villages in Eastern and Southern Ukraine. Participants: UPROANF attending clinics completed voluntary self-report surveys reporting demographics, prior health diagnoses, and PTSD symptom severity ( n = 450; Mean age = 53.66; 72.0% female). Exposure: Participants were exposed to Russian occupation of Ukrainian villages. Main outcome and measures: The PTSD Checklist for the DSM-V (PCL-5) with recommended diagnostic threshold (i.e. 31) was utilized to assess PTSD prevalence and symptom severity. ANCOVA was used to examine hypothesized positive associations between (1) HTN and (2) loneliness and PTSD symptoms (cumulative and by symptom cluster). Results: Between 47.8% and 51.33% screened positive for PTSD. Though cumulative PTSD symptoms did not differ based on HTN diagnostic status, those with HTN reported significantly higher PTSD re-experiencing symptoms ( b = 1.25, SE = 0.60, p = .046). Loneliness was significantly associated with more severe cumulative PTSD symptoms ( b = 1.29, SE = 0.31, p < .001), re-experiencing ( b = 0.47, SE = 0.12, p < .001), avoidance ( b = .18, SE = 0.08, p = .038), and hypervigilance ( b = 0.29, SE = 0.13, p = .036). Conclusions and relevance: PTSD prevalence was higher than other war-exposed populations. Findings highlight the urgent mental health burden among UPROANF, emphasizing the need for integrated care models addressing both trauma and physical health. Given the significance of loneliness as a risk factor, findings suggest the potential for group-based, mind-body interventions to holistically address the physical, mental, and social needs of this highly traumatized, underserved population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".