Prevalence of stress, anxiety, and symptoms of post-traumatic stress disorder among Ukrainians after the first year of Russian invasion: a nationwide cross-sectional study
Bibliographic record
Abstract
Background: In February 2022 the Russian federation started a new invasion of Ukraine as an escalation of the ongoing war since 2014. After nine years of war and the COVID-19 pandemic, the mental health state of Ukrainians requires systematic monitoring and relevant action. The aim of present study was to investigate the state of mental health among Ukrainians assessing the levels of stress, anxiety, and post-traumatic stress disorder (PTSD) prevalence in not displaced persons (NDPs), internally displaced persons (IDPs), and refugees abroad. Methods: This study was designed as an online survey arranged in the 9-12 months after the start of the new invasion of Ukraine and includes sociodemographic data collection, evaluation of stress intensity by Perceived Stress Scale (PSS-10), anxiety with General Anxiety Disorder (GAD-7), and symptoms of post-traumatic stress disorder with PTSD Check List (PCL-5). Findings: The sample size of 3173 Ukrainians consisted of 1954 (61.6%) respondents that were not displaced persons (NDPs), 505 (15.9%) internally displaced persons within Ukraine (IDPs), and 714 (22.5%) refugees that left Ukraine. Moderate and high stress was prevalent among 68.2% (1333/1954) and 15.5% (302/1954) of NDPs, 64.4% (325/505) and 21.6% (109/505) of IDPs, and 64.7% (462/714) and 25.2% (180/714) of refugees, respectively. Moderate and severe anxiety was prevalent among 25.6% (500/1954) and 19.0% (371/1954) of NDPs, 25.7% (130/505) and 23.4% (118/505) of IDPs, and 26.2% (187/714) and 25.8% (184/714) of refugees. High levels of PTSD (33 and higher) were prevalent among 32.9% (642/1954) of NDPs, 39.4% (199/1954) of IDPs, and 47.2% (337/714) of refugees. DSM-V criteria for PTSD diagnosis was met by 50.8% (992/1954) of NDPs, 55.4% (280/505) of IDPs, and 62.2% (444/714) of refugees. Only 7.2% of the respondents reported no or mild stress, anxiety, and PTSD levels within the sample. Interpretation: The lowest stress, anxiety, and PTSD severity was observed among NDPs, with significantly higher levels among IDPs and the highest among refugees. Being forcibly displaced from the previous living area and, especially, entering a new cultural environment significantly contributes to the mental health issues caused by war exposure and witnessing. Funding: Ministry of Education and Science of Ukraine.
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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 | 0.001 | 0.001 |
| 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".