Students’ Perceived Well-Being and Online Preference: Evidence from Two Universities in Vietnam during COVID-19
Bibliographic record
Abstract
University education is still being impacted two years after the COVID-19 outbreak. We performed a rapid survey in February 2022 at two public universities in Vietnam to examine the effects of the pandemic on well-being and the factors that may associate with online class preference among university students as well as to investigate the need for support to improve resilience. A web-based survey included 1589 undergraduate students in total. Both quantitative and qualitative data analysis was carried out. Overall, approximately a quarter of respondents said that they perceived an influence on their health, 42.9% expressed stress, and more than 70% reported worrying about the future. In total, 61.9% of the respondents reported having satisfaction with online classes, while over half of them preferred a program of 50% online classes. Students who live in an urban area, are female, have had pre-COVID-19 campus life experience, have decreased income, and/or experience low online satisfaction and over-information may be in need of more support. The results show implications for universities to consider policies addressing well-being and post-pandemic online education. Providing support to university students to improve their resilience against the impact on their studying, campus life, health, and well-being should be prioritized during and post-pandemic.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".