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Record W4403541881 · doi:10.1080/03043797.2024.2417345

Exploring mental health experiences and supports among international engineering undergraduate students – insights to inform university support

2024· article· en· W4403541881 on OpenAlexafffundabout
Kristine Arreola, S.H. Cho, C.T. Phan, Karen V. Unger, Wen‐Pin Chang, Shu‐Ping Chen

Bibliographic record

VenueEuropean Journal of Engineering Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMental healthEngineering educationMedical educationPsychologyEngineering ethicsEngineeringMathematics educationPedagogyEngineering managementMedicine

Abstract

fetched live from OpenAlex

This study explores the mental health perceptions, expectations for mental healthcare, and use of existing mental health services among international engineering students at a Western Canadian university. Employing a phenomenological qualitative approach, semi-structured interviews were conducted with 18 international undergraduate engineering students to understand their mental health challenges and service usage. The thematic analysis revealed three significant themes: students’ understanding of mental health; their experiences within the engineering programme, and recommendations to enhance and facilitate a positive mental health experience at the university. Findings suggest that mental healthcare for international engineering students should be responsive to the students’ unique needs, providing support and accessible services during their transition from their home countries and recognising the impact of the environmental and cultural factors on their experiences as members of the Faculty of Engineering and the broader university community.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0070.002
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.310
Teacher spread0.277 · 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 routes3
Has abstractyes

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