Predictors of Depression among College Students in the Early Stages of the COVID-19 Pandemic
Bibliographic record
Abstract
College students are disproportionately impacted by depression compared to the general population. The purpose of this study was to determine the predictors of depression among college students during the COVID-19 pandemic to inform interventions. This cross-sectional study surveyed students at a large, diverse university in the southwest United States. Students provided information regarding the severity of their depression symptoms over the past two weeks (dependent variable) along with independent demographic and educational variables (age, sex, sexual orientation, grade point average, number of credits taken, first-generation college student status, race/ethnicity, and employment status), perceived stress, hours of sleep, physical fitness, and minutes of physical activity. Univariate and multivariate linear regression analyses were conducted. Variables that were significantly associated with depression in the multiple linear regression included stress, identifying as Asian, hours of sleep, and age. There is a need for stress management and mental health promotion interventions targeting college students. Additional interventionals should also focus on those more at risk, including those who identified as Asian (almost three times more likely to report depression compared with White students) and younger college students. We also found a need to promote sleep hygiene.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".