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Record W4401919837 · doi:10.1192/j.eurpsy.2024.360

Mental health impact of the Russian-Ukraine war on Canadian residents with or without Ukrainian descent

2024· article· en· W4401919837 on OpenAlexaffabout
A. Belinda, Reham Shalaby, Yifeng Wei, Vincent I. O. Agyapong

Bibliographic record

VenueEuropean Psychiatry · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsDalhousie UniversityUniversity of Alberta
Fundersnot available
KeywordsUkrainianMental healthPolitical scienceDescent (aeronautics)MedicineGeographyPsychiatry

Abstract

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Introduction War tends to produce fear. The devastating and traumatic occurrences of war can have both short- and long-term effects on the mental well-being of populations. Russia’s invasion of Ukraine indirectly affects all populations, especially individuals of Ukrainian descent. Objectives To assess the mental health impact of the Russian invasion of Ukraine on Canadian residents who subscribed to ‘Text4Hope Ukraine’ program and to ascertain if there are differences in mental health impacts between those with and without Ukrainian descent. Methods Canadians were invited to self-subscribe to the text messaging program. An online survey was used to collect sociodemographic, war-related, and clinical information; stress, resilience, likely anxiety disorder and likely depressive disorder from subscribers. Outcome measures included baseline scores using validated scales. Data were analyzed using SPSS Version 25. To examine the association of psychological problems with the sociodemographic and war-related factors, univariate analysis using the Chi-square/Fishers Exact test was performed with two-tailed significance (p ≤ .05). An independent sample t-test with two-tailed significance (p-value ≤ 0.05) was employed to assess the differences in the respective mean scores of the psychological problems across the two groups. The first group represents the participants who did not have citizenship or ancestors from Ukraine (NUk), while the second group represents the respondents are Ukrainian who either have previously held citizenship or have ancestors/family from Ukraine (Ukr). No imputation of missing data and reported data represents the complete responses Results Study findings reflected prevalence of low resilience (59.7%), moderate to high stress (87.5%), likely Generalized Anxiety Disorder (45.8%) and likely Major Depressive Disorder (38.9%). Respondents who identified as female had a higher likelihood of presenting with low resilience (χ2(1) = 5.68, p = .02) and likely Generalized Anxiety Disorder (χ2 (1) = 4.85, p = .03) compared to male respondents. There was no statistically significant difference in the mean scores of the four psychological problems based on any of the variables that suggest Ukrainian descent or not (p>.05). Conclusions War can have negative impacts on all populations irrespective of their location, or association of individuals with the impacted country. This study provides valuable insights into the mental health impact of the Russian invasion of Ukraine on a specific sample of Canadian residents who subscribed to the ‘Text4Hope Ukraine’ text messaging program. This information is relevant when planning mental health intervention for this population. Governments should target and provide adequate mental health and psychosocial support or interventions for global populations at risk during war. Disclosure of Interest None Declared

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.019
GPT teacher head0.336
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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