Real-time Mental Health Impact of the COVID-19 Pandemic on College Students: Ecological Momentary Assessment Study
Bibliographic record
Abstract
BACKGROUND: College students' mental health may be disproportionally affected by the COVID-19 pandemic because of the abrupt shift off campus and subsequent loss of a social network and potential long-term impact on job prospects. OBJECTIVE: We sought to assess the nature of COVID-19's mental health impact among a sample of undergraduates who were experiencing the pandemic as it occurred in real time. METHODS: In total, 140 college students completed smartphone-based ecological momentary assessments of anxiety and optimism related to COVID-19 and other generic mental health variables 6 times daily. RESULTS: Participants completed >23,750 surveys. Overall, >75% of these surveys indicated at least some level of anxiety about COVID-19. On average, the proportion of responses each day at the highest levels of anxiety about COVID-19 was 7 times greater than the proportion of responses at the highest levels of non-COVID-19-specific anxiety. Structural change analyses indicated a significant downward trend in COVID-19 anxiety after the first week of June, but even at the lowest point, >15% of the participants in the sample still reported high levels of COVID-19 anxiety each day. Participants felt more anxious about COVID-19 on days when the number of new cases and deaths due to COVID-19 were higher. When participants felt anxious about COVID-19, they also felt sad, anxious (in general), and had a greater desire to drink and use drugs. Participants felt more optimistic about COVID-19 when they received more support from others and from their university. CONCLUSIONS: This study demonstrated the widespread mental health impact that COVID-19 has had on college students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".