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Record W7038721366

International Students and Mental Health

2024· article· en· W7038721366 on OpenAlexaboutno aff

Bibliographic record

VenueSOURCE Sheridan's Institutional Repository (Sheridan College) · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicColeoptera Taxonomy and Distribution
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPerceptionFace (sociological concept)Computer-assisted web interviewingStudy abroad
DOInot available

Abstract

fetched live from OpenAlex

This research seeks to explore the barriers and challenges international students face in Canada. The study was conducted to understand how these barriers and challenges effect international student’s mental health and what their perceptions are about utilizing mental health and well-being services. The approach used in this research was quantitative, an online anonymous survey conducted that received in total of 30 responses. The results show that participants experience financial challenges, loneliness, and housing. Participants have financial difficulties and limited knowledge and awareness in accessing mental health and well-being services. Further improvements have been encouraged towards mental health and well-being services by making it more accessible online or in-person and prefer in-person counselling and therapy. The research helped grow a deeper understanding and identified underlying issues, grasping authentic preferences of utilizing mental health and well-being services to meet their needs. From the results, our suggestion is to decrease tuition fees for international students as majority of financial difficulties stem from this issue.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.508
Threshold uncertainty score0.989

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.004
Scholarly communication0.0070.002
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0220.001

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 head0.012
GPT teacher head0.240
Teacher spread0.229 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueSOURCE Sheridan's Institutional Repository (Sheridan College)Same topicColeoptera Taxonomy and DistributionFrench-language works237,207