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Record W3112079278 · doi:10.47678/cjhe.vi0.188815

Mental Health Status and Help-Seeking Strategies of Canadian International Students

2020· article· en· W3112079278 on OpenAlexaffvenueabout
Danielle de Moissac, Jan Marie Graham, Kevin Prada, Ndèye Rokhaya Gueye, Rhéa Rocque

Bibliographic record

VenueCanadian Journal of Higher Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of WinnipegBrandon UniversityUniversité de MontréalUniversité LavalUniversité du Québec à MontréalUniversité de Saint-Boniface
Fundersnot available
KeywordsMental healthPsychologyCoping (psychology)Scale (ratio)Mental health literacyHigher educationMedical educationDistressClinical psychologyMedicinePsychiatryMental illnessPolitical science

Abstract

fetched live from OpenAlex

International students are at heightened risk of developing psychological distress, yet little research has been conducted on their mental health or support needs. This quantitative study focused on undergraduate students at two mid-sized universities in Manitoba, Canada. Online and paper surveys were completed by 932 participants, of whom 21% identified as international students. This paper, descriptive in nature, outlines the sociodemographic profiles, current mental health status, psychological characteristics, and coping strategies of international students compared to domestic students in each institution. Data show that international students are more likely to report excellent mental health, score higher on the mental health scale, and report higher life satisfaction, higher self-esteem, and more positive body image than domestic respondents. However, they are less likely to talk about their hardships. Providing culturally-adapted supports that take into consideration ethnolinguistic differences, religious practice, and mental health literacy will better meet the needs of international students on campus.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.042
GPT teacher head0.369
Teacher spread0.327 · 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 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

Citations28
Published2020
Admission routes3
Has abstractyes

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