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Record W2110478729 · doi:10.5539/ass.v11n15p159

Resilience, Optimism and Social Support among International Students

2015· article· en· W2110478729 on OpenAlexvenueno aff
Fatemeh Sabouripour, Samsilah Roslan

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsOptimismPsychologySocial supportPsychological resilienceScale (ratio)Test (biology)Resilience (materials science)Social psychologyApplied psychology

Abstract

fetched live from OpenAlex

This study focuses on the examination of the relationship between resilience, optimism and social support amonginternational students. International students who are studying as foreign students tend to experience greaterstress and anxiety during their study. They need to adjust to the new environment and overcome challenges. Theresilience level of students is very important as it can help them adjust their life pressures and stresses. Thecurrent study aims to look at the levels and patterns of resilience, optimism and social support amonginternational students. The instruments used were Connor-Davidson Resilience Scale (CD-RISC) for resilience,Life Orientation Test Revised (LOT-R) for optimism and Multidimensional Scale of Perceived Social Support(MSPSS) for social support. A total of 291 international students were involved in the study. The findingsrevealed a significant difference in the resiliency level across races, with African students scoring higher thanothers. The regression analysis employed showed that optimism (B=. 593) and social support (B=. 204) aresignificant predictors of resilience.

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)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.438
Teacher spread0.405 · 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

Citations84
Published2015
Admission routes1
Has abstractyes

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