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Record W4404985093 · doi:10.1177/21582440241300522

“There’s a Certain Loneliness of Being in a Space That Does Not Relate to You”: The Resilience and Mental Health Experiences of International Students During the COVID-19 Pandemic

2024· article· en· W4404985093 on OpenAlexaffabout
Shailoo Bedi, Jillian Roberts, Celeste Duff

Bibliographic record

VenueSAGE Open · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLonelinessMental healthPandemicCoronavirus disease 2019 (COVID-19)PsychologyPsychological resilienceResilience (materials science)2019-20 coronavirus outbreakSpace (punctuation)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Social psychologyDevelopmental psychologyPsychiatryMedicineVirologyOutbreakComputer science

Abstract

fetched live from OpenAlex

Research indicates that the adverse effects on post-secondary students from the COVID-19 pandemic are unprecedented on a global scale. Specifically, there is limited research that focuses on international students’ mental wellness, resilience, and well-being experiences during the COVID-19 pandemic. This study qualitatively explores the resilience and mental wellness experiences of international university students at a mid-size, research-intensive, public university in British Columbia, Canada. Nine international students, between the ages of 18 and 30, participated in narrative-style interviews. Data were analyzed by using thematic analysis and applying a resilience lens framework. The findings highlight students’ mental wellness challenges and the key factors that were instrumental for supporting their mental wellness and enacting their resilience. These findings help to mitigate the negative impacts that can result from studying during a pandemic and offers recommendations for universities on how to support international students’ overall wellbeing, particularly during significant disruption and isolation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.919

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.045
GPT teacher head0.457
Teacher spread0.412 · 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 designQualitative
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

Citations2
Published2024
Admission routes2
Has abstractyes

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