Assessing Financial Well-being of Undergraduate University Students during COVID-19 Pandemic
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".