When social isolation is nothing new: A longitudinal study on psychological distress during COVID-19 among university students with and without preexisting mental health concerns.
Bibliographic record
Abstract
The coronavirus disease 2019 global pandemic has had an unprecedented impact on college and university campuses internationally (e.g., widespread campus closures, transitions to online learning).Postsecondary students, who were already a developmentally vulnerable population, are now facing additional new challenges, which could lead to increased mental health concerns.However, there is a paucity of research on the psychological impacts of COVID-19, or who may be most at risk, among postsecondary students.To address these gaps in the literature, we recontacted a sample of 773 postsecondary students (74% female, Mage =18.52) who previously completed a survey on student mental health in May 2019, again in May 2020.Students filled out an online survey at both time points, reporting on their recent stressful experiences and mental health.Although we expected that students with preexisting mental health concerns would show increased psychological distress during the pandemic, this hypothesis was not supported.Instead, repeated-measures analyses demonstrated that students with preexisting mental health concerns showed improving or similar mental health during the pandemic (compared with one year prior).In contrast, students without preexisting mental health concerns were more likely to show declining mental health, which coincided with increased social isolation among these students.Our findings underscore that colleges and universities will not only need to continue to support students with preexisting mental health needs but also prioritize early prevention and intervention programming to mitigate the impacts of COVID-19 on students with increasing psychological distress, potentially stemming from increasing social isolation in response to the pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".