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Record W4317213447 · doi:10.5539/hes.v13n1p24

Assessing Financial Well-being of Undergraduate University Students during COVID-19 Pandemic

2023· article· en· W4317213447 on OpenAlexaffvenueabout
Luai Al‐Labadi, Jin-Young Hur, Kyuson Lim, Nitya Srivastava

Bibliographic record

VenueHigher Education Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsHigher educationPandemicGovernment (linguistics)PsychologyMedical educationCurriculumFinanceCoronavirus disease 2019 (COVID-19)Public relationsBusinessPolitical scienceEconomic growthEconomicsPedagogyMedicine

Abstract

fetched live from OpenAlex

The COVID-19 pandemic poses financial challenges for students worldwide, especially for those in higher education where free and universal access is not guaranteed. Students in developed economies, a long-neglected group for pandemic studies, are not exceptions. The motivation of this study is to examine subjective financial well-being of undergraduate students during the COVID-19 pandemic. Accordingly, we conducted a self-administered survey composed of 31 questions that assesses students’ demographic information and impact of COVID-19 on their financial status. The sample includes 655 students enrolled at the University of Toronto Mississauga during the academic year 2020-2021.The survey results show that most students are concerned about the impact of COVID-19 on the ability to meet overall financial obligations or essential needs. Using factor analysis, we further identified relevant matters of subject associated with this concern on subjective financial well-being: 1) concern on tuition costs and financial obligation; and 2) concern on living and traveling costs. The result clearly identifies the associations between university students’ perceptions of financial well-being and its associated matters, and how these relations differ by students’ demographic information. We conclude that educational leadership, local community, and the government should use their judgement to reduce students’ financial distress by offering different types of financial supports with regards to student demographics.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.157
GPT teacher head0.504
Teacher spread0.347 · 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 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

Citations1
Published2023
Admission routes3
Has abstractyes

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