Mental health and coping among graduate students during the COVID-19 pandemic: a gender-based analysis
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has profoundly impacted the mental health of young adults in Canada, with research showing high rates of depression and anxiety symptomatology. Graduate students, who already experience elevated mental health challenges, represent a particularly vulnerable population-yet research examining their experiences during the pandemic remains limited. This study aims to investigate mental health and well-being outcomes, negative impacts, coping strategies, and gender differences among Canadian graduate students during the COVID-19 pandemic. Methods: A cross-sectional survey was conducted among Canadian graduate students (N = 261) to assess mental health symptoms, well-being, negative impacts, coping strategies, and gender differences during the COVID-19 pandemic using a series self-report of questionnaires (e.g. BDI, BAI, DASS-S). Data were analyzed using descriptive statistics, chi-square tests and t-tests. Results: Findings revealed significant mental health challenges among Canadian graduate students during the pandemic, with high rates of depression, anxiety, and stress reported. Female students reported worse mental health outcomes and experienced greater negative impacts compared to males. Coping strategies predominantly involved avoidant behaviors, such as watching TV and using social media, with gender differences in coping strategies. Discussion: Compared to pre-pandemic findings, graduate students in this pandemic sample reported elevated rates of mental health challenges. Women appeared to be disproportionately impacted, reflecting the heightened mental health burden they reported during this period. Avoidant coping strategies were most commonly used-aligning with the socially isolating conditions of the pandemic-with notable gender differences in types of strategies employed. Conclusion: The COVID-19 pandemic seems to have exacerbated the mental health crisis among Canadian graduate students, with higher rates of depression, anxiety, and stress reported compared to pre-pandemic findings. Female students face heightened challenges, emphasizing the need for gender-sensitive support strategies. Universities should prioritize mental health support and promote healthy coping mechanisms to address the impacts of the pandemic on graduate student well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".