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Domestic and international experience in the psychological rehabilitation of military families with post-traumatic stress disorder

2025· article· W7117459593 on OpenAlexaboutno aff
Volodymyr Vasyshchev

Bibliographic record

VenueThe Psychology of Security · 2025
Typearticle
Language
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialRehabilitationUkrainianMilitary personnelMilitary serviceService memberService (business)Psychosocial support

Abstract

fetched live from OpenAlex

The full-scale war against Ukraine has led to extensive psychological trauma among the population, especially service members and their families, threatening national socio-psychological stability. Despite existing support programs, Ukraine lacks an integrated psychosocial rehabilitation system for military families that ensures continuous assistance from combat to reintegration. The issue is compounded by staff shortages, poor interagency coordination, and the absence of unified psychological support standards. Meanwhile, the experiences of the United States, Canada, the United Kingdom, and Israel show the effectiveness of family-oriented models combining governmental, public, and volunteer resources. Adapting these approaches to Ukrainian realities is crucial for developing a national system of psychological rehabilitation for military families within the moral and psychological support framework of the security and defense sector.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.043
GPT teacher head0.432
Teacher spread0.389 · 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 designNot applicable
Domainnot available
GenreReview

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 routes1
Has abstractyes

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