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Record W7010689387

International Student’s Recommendations To Improve Mental Health Support Services in Post Secondary Educational Institutions in Canada

2025· article· en· W7010689387 on OpenAlexaboutno aff

Bibliographic record

VenueSOURCE Sheridan's Institutional Repository (Sheridan College) · 2025
Typearticle
Languageen
FieldEngineering
TopicEngineering and Materials Science Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthStigma (botany)Help-seekingStudy abroadCultural competenceHealth servicesLanguage barrierMental illnessInternational health
DOInot available

Abstract

fetched live from OpenAlex

The research study conducted was to find out: What are international students' recommendations to improve mental health support services in post-secondary educational institutions in Canada? This was a quantitative study that also had open ended questions so that participants may be able to add additional information if they had wished. The findings revealed were that there is a stigma that international students deal with when it comes to mental health, there is also a need of having counselors that speak different language as there are language barriers. Students from other countries don’t seem to think that mental health is that important, as well as must deal with cultural barriers and don’t actively seek out services when they are having mental health issues. A lot of international students in this study did not know about mental health services or were not interested. This study has been found to align with other studies on international students’ experience navigating mental health stigma and the need for more accessible support services.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.374

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.003
Scholarly communication0.0070.002
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.004
GPT teacher head0.240
Teacher spread0.236 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueSOURCE Sheridan's Institutional Repository (Sheridan College)Same topicEngineering and Materials Science StudiesFrench-language works237,207