Mental health screening in refugees communities: Ukrainian refugees and their post-traumatic stress disorder specificities
Bibliographic record
Abstract
Post-Traumatic Stress Disorder (PTSD) symptoms are often consequences of war conflicts that generate trauma in people, resulting from the loss of family, home and belonging. It is associated with the forced migration and lack of structured facilities or health professionals in the hosting country, as a result of varying degrees of clinical assessment and sociocultural intervention. To evaluate the lack of resources for refugees from Ukraine, as well for clinical and research purposes, this study examined the validity and psychometric properties of one Ukrainian adapted version of the PCL-5 (Check-List for PTSD) and the convergent validity for the four factor model of DSM-5 for PTSD. Varying reactions to war events and war zone characterization are among the variables expected to produce PTSD group differences in these refugees. Thus, PTSD was investigated in 77 Ukrainian refugees, who had resided in Portugal for a minimum of four weeks, and who answered the 20-item PCL-5 scored on a Likert scale. The PCL-5 revealed satisfactory convergent validity overall, and in the clusters of the four factor model, based on the DSM-5. Additionally, the role of the participants’ age, sex, education level, time of residence in the host country, and the average income before the russo-ukrainian war was evaluated. 34 of 77 met the criteria for PTSD with a good fit for the four factor model (as the original of DSM-5 for PTSD) after the Confirmatory Factorial Analysis (CFA) was conducted. Mediating factors found for PTSD: female, young adults, high-income in Ukraine, low level of education, married, solo migration. Further investigation into factor modeling and clinical practice are discussed.
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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 | 0.001 | 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".