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Record W4387365783 · doi:10.15421/102206

MENTAL AND SOCIAL HEALTH OF HIGHER EDUCATION SEEKERS IN THE WAR-TIME

2022· article· en· W4387365783 on OpenAlexaboutno aff
Вікторія Іванівна Лазаренко

Bibliographic record

VenueCai fu bi ji · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Social Development in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthAnxietyPsychologyQuarter (Canadian coin)PsychiatryClinical psychology

Abstract

fetched live from OpenAlex

Abstract. The paper is devoted to the investigation of mental and psychological health of students of Oles Honchar Dnipro National University in the war-time.
 The urgency of the research problem on the nature of the mental, psychological and social health. The human health as a condition of physical, mental and social well-being is considered from the perspective of the holistic approach. The findings of empirical studies about the level of mental and social health of students of Oles Honchar Dnipro National University are represented. The analysis of mental and social health of 1-3-year students of different specialties in Oles Honchar Dnipro National University.
 The scientific works of domestic and foreign authors concerning the research of values-based and semantic orientations and professional self-realization have been analyzed. It is empirically proved that, in general, the students have the average indicators. However, the level of mental health of some students is quite low and only a small part of youth has no social anxiety disorder. Over half of the students have high and above average indicators, but a certain number of students have clinical implications of social anxiety disorder. Subclinical and clinical implications of anxiety are typical of the quarter of students of the 1-3 years of study of Oles Honchar Dnipro National University. The third of them has subclinical and clinical implications of depression.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.357
Teacher spread0.331 · 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 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
Published2022
Admission routes1
Has abstractyes

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