The Mental Health and Well-Being of Master’s and Doctoral Psychology Students at an Urban Canadian University
Bibliographic record
Abstract
Aim/Purpose: Although the high rates of stress and psychological distress in graduate students has been well-documented, Canadian samples are underrepresented in the extant literature. The present study explores prevalence rates of burnout and psychological distress in a sample of psychology master’s and doctoral students at a university in a large urban Canadian city, as well as factors relating to their well-being, social support and stress. Background: There are economic and productivity setbacks stemming from high stress and mental health challenges. Burnout and psychological distress of graduate students are associated with hindered academic progress, mental and physical health challenges, and reduced productivity. Further, emotionally exhausted doctoral students are at heightened risk for non-completion of their degrees. Methodology: Sixty-two psychology graduate students completed an online survey that assessed burnout, psychological distress (anxiety, depression, and stress symptoms), perceived social support, collegiate sense of community, financial strain, and rank-ordered nine domains of graduate school stressors. Contribution: The present paper contributes to the body of knowledge that graduate students residing in an urban Canadian city experience high rates of burnout and psychological distress. High levels of social support outside the academe were not protective factors in mitigating burnout. Findings: Participants reported high levels of perceived social support and sense of community. However, over half (60%) of respondents met criteria for burnout, and one in three students met criteria for problematic levels of stress, anxiety, and/or depression. In a rank ordering question, “thesis, dissertation or other research”, “classwork” and “finances” ranked in the top three most stressful aspects of graduate school for respondents. Recommendations for Practitioners: Graduate students experience unique stressors related to their mental health and well-being that differ from undergraduate students and young working professionals. Mental health practitioners may be better equipped to support graduate students with knowledge of these specific factors impacting mental health and well-being. Recommendation for Researchers: Based on these findings, four areas of recommendations for psychology graduate institutions and training programs are discussed. These recommendations highlight the need for change across systemic levels and call for integrative efforts to improve wellbeing for psychology graduate students. Impact on Society: Enhancement of doctoral student well-being could contribute to long-term benefits in academia and in higher education. Future Research: The study took place before the emergence of COVID-19, which has undoubtably impacted graduate students globally. Research on student experiences during this unprecedented time is needed, as are additional supports (e.g., virtual programming to reduce social isolation; contingency plans for data collection).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 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) | 0.003 | 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".