The early impact of the global lockdown on post-secondary students and staff: A global, descriptive study
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to gain a preliminary, broad-level understanding of how the first lockdown impacted post-secondary students, faculty, and staff worldwide. METHODS: The data were obtained via a global online cross-sectional questionnaire survey using a mixed-method design and disseminated to university students, faculty, and staff from April to November 2020. The data were categorized in four themes/categories: (1) social life and relationships, (2) access to services, (3) health experiences, and (4) impact on mental health well-being. RESULTS: The survey included 27,804 participants from 121 countries and 6 continents. The majority of participants were from Europe (73.6%), female (59.2%), under 30 years of age (64.0%), living in large urban areas (61.3%), %), and from middle-income families (66.7%). Approximately 28.4% of respondents reported that the lockdown negatively impacted their social life, while 21.2% reported the lockdown had a positive impact. A total of 39.2% reported having issues accessing products or services, including essentials, such as groceries, or medical services. In addition, respondents reported an increase in stress and anxiety levels and a decrease in quality of life during the first 2 weeks of the lockdown. CONCLUSIONS: The COVID-19 pandemic and lockdown measures had an evident impact on the lives of post-secondary students, faculty, and staff. Further research is required to inform and improve policies to support these populations at both institutional and national levels.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".