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Record W4401955553 · doi:10.1080/15363759.2024.2392273

International Students’ Stories of Pandemic Self-Isolation: Perspectives at a Christian University

2024· article· en· W4401955553 on OpenAlexaffabout
Dieu Hack‐Polay, Dannie Brown

Bibliographic record

VenueChristian Higher Education · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsCrandall University
Fundersnot available
KeywordsPandemicChristianityIsolation (microbiology)PedagogySociologyPsychologyMathematics educationCoronavirus disease 2019 (COVID-19)Religious studiesPhilosophyMedicine

Abstract

fetched live from OpenAlex

This research aims to examine the experiences of international students taking courses during the COVID-19 pandemic amidst lockdowns, self-isolation, and the closure of international borders. We interviewed 15 students, mainly from South Asia, attending university in Atlantic Canada who could not return to their countries of origin before the travel restrictions were imposed. The findings expose major mental health issues the stranded international students faced and the impact on their well-being as well as educational attainment. The study has implications for mental health support systems in Christian universities but has also revealed that Christian compassion was a key coping mechanism for affected students. To the authors’ knowledge, this study is the first to examine the experience of international students left stranded by COVID-19 restrictions in Atlantic Canada.

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)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0290.021
Scholarly communication0.0130.005
Open science0.0020.015
Research integrity0.0040.014
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 head0.036
GPT teacher head0.403
Teacher spread0.367 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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