Long-Term Effects of the COVID-19 Pandemic: Emotional Regulation, Psychological Symptoms, and College Adjustment
Bibliographic record
Abstract
The COVID-19 pandemic was responsible for an unprecedented increase in psychological problems among post-secondary students worldwide. Drawing on data from a repeated cross-sectional (RCS) project, this study investigated changes in psychological symptoms, emotional regulation (cognitive reappraisal and emotional suppression), and academic, social, and personal-emotional college adjustment, and associations between these variables among students in two countries during the phases of lockdown (2021), lifting of restrictions (2022), and the endemic phase (2023). University students in Canada (n = 1014) and Spain (n = 447) completed online surveys during these periods. Students in both countries reported significant declines in perceived COVID-19 stress across the pandemic phases. In comparison with pre-pandemic rates, elevated psychological symptoms remained constant. There were some country differences, but sex differences were consistent. Psychological symptoms mediated the association between cognitive reappraisal and the adjustment measures among Canadian students during each pandemic period. Alternatively, they mediated the linkages of maladaptive emotional suppression with academic, social, and personal-emotional functioning of Spanish students at every phase, but only during the lifting of restrictions and the endemic phase for Canadian students. The results indicate the complexity of country and context in the role of emotional regulation during uncontrollable conditions and provide directions for intervention in stressful situations, including adjustment to university and future disastrous environmental events.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".