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Record W4417100373 · doi:10.1177/20551029251405054

Time spent following the Russian-Ukranian war (RUW) and psychological distress: The role of sleep problems

2025· article· en· W4417100373 on OpenAlexafffund
Taina Hintsa, Petri Karkkola, Juhani Julkunen, Esther R. Greenglass

Bibliographic record

VenueHealth Psychology Open · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsYork University
FundersYork University
KeywordsSleep (system call)Association (psychology)Psychological distressEmotional distressStructural equation modelingDistress

Abstract

fetched live from OpenAlex

Background: The Russian-Ukranian war (RUW) broke in 2022. Finland is a neighboring country of Russia. People in Finland could be assumed to be especially vulnerable to war-related stress. We examined the relationship between time spent following the RUW from media, sleep problems and psychological distress in university students. Methods: The participants were university students who responded anonymously to a questionnaire. They reported their age, gender, time spent following RUW, anxiety, depressive symptoms, and sleep problems. Statistical analyses were conducted using SPSS and Mplus for structural equation modeling. Results: The time spent following RUW from media was associated with greater psychological distress, and more sleep disturbances. Sleep disturbances accounted for more than 12% of the association between time spent following RUW and psychological distress. Conclusions: Present findings suggest that sleep problems should be taken into account when supporting students. Support programs should emphasize the importance of sleep in psychological well-being.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.470
Teacher spread0.403 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2025
Admission routes2
Has abstractyes

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