Coping With the COVID-19 Pandemic: Examining Gender Differences in Stress and Mental Health Among University Students
Bibliographic record
Abstract
The COVID-19 pandemic has imposed a wide variety of unprecedented challenges, many of which appear to be disproportionately affecting the mental health and well-being of young adults. While there is evidence to suggest university students experience high rates of mental health disorders, less is known about the specific impacts of the COVID-19 pandemic on student mental health and how they are coping with this stress. To address this gap, we conducted an online study among undergraduate students (n = 366) to examine the impact of the COVID-19 pandemic on academics, social isolation, and mental health, as well as the extent to which they have been implementing a variety of coping strategies. The pandemic had a more pronounced negative effect on female students' academics, social isolation, stress and mental health compared to male counterparts. Moreover, for females, frequent use of social media as a coping mechanism was associated with greater perceived negative impacts on their academic performance and stress levels, compared to males. However, frequent social media use related to similar negative mental health effects for both males and females. While male and female students both reported using substances to cope, for males the use of cannabis was associated with greater negative impacts on academic outcomes, stress and mental health compared to females. These findings highlight the need for adequate student support services across the post-secondary sector, and point to the importance of gender informed interventions to address the impacts of the COVID-19 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".