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Record W4388998698 · doi:10.1038/s41598-023-47840-z

Association between mental health, psychological characteristics, and motivational functions of volunteerism among Polish and Ukrainian volunteers during the Russo-Ukrainian War

2023· article· en· W4388998698 on OpenAlexaff
Agata Chudzicka‐Czupała, Soon‐Kiat Chiang, Clara M. Tan, Nadiya Hapon, Marta Żywiołek‐Szeja, Liudmyla Karamushka, Mateusz Paliga, Zlatyslav Dubniak, Roger S. McIntyre, Roger Ho

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsBrain and Cognition Discovery FoundationUniversity of Toronto
FundersFundacja na rzecz Nauki Polskiej
KeywordsPsychosocialUkrainianCoping (psychology)PsychologyClinical psychologyAnxietyMental healthFeelingPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

The Russo-Ukrainian War has led to a humanitarian crisis, and many people volunteered to help affected refugees. This cross-sectional survey study investigates the relationships between the psychological impact of participation, coping mechanisms, and motivational functions of volunteering during the Russo-Ukrainian War among 285 Ukrainian and 435 Polish volunteers (N = 720). Multivariate linear regression was used to examine relationships between motivational functions and psychosocial and demographic characteristics. Ukrainian volunteers reported significantly higher Hyperarousal and Avoidance, Depression, Anxiety, and Stress, Problem-focused, Emotion-focused, and Avoidant coping, as well as total scores of Hardiness and Psychological Capital than Polish counterparts. Linear regression analysis found that Impact of the Event Scale results, Coping with Stress, being a female, unemployed, and religious were significantly associated with higher motivational functions. Ukrainian volunteers could significantly reduce negative feelings and strengthen social networks and religious faith by volunteering, while Polish volunteers were significantly more likely to gain skills and psychosocial development from helping others.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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.

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

Citations11
Published2023
Admission routes1
Has abstractyes

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