The Mental Well-Being of Graduate Students in Canada: A Scoping Review
Bibliographic record
Abstract
Objective: To review the literature exploring the mental health of graduate students in Canada. Data Source: Articles identified in EMBASE, CINAHL, PsycInfo, Medline, Sociological Abstracts, Nursing and Allied Health, and ERIC. Study Inclusion and Exclusion Criteria:Two independent reviewers screened articles that: (1) focused on graduate students’ mental wellbeing; (2) used empirical study designs (3) were published in English; (4) were conducted in Canada. Data Extraction: Twenty-two articles met the inclusion criteria. Data was extracted on the following variables: author(s); year of publication; study design; methodology; clinical characteristics of participants; number of participants; demographic characteristics of participants, journal discipline and university. Data Synthesis: We used Dedoose, a qualitative data management program, to perform qualitative content analysis and characterize the data and identify emerging themes. Results: The content analysis led to three related themes from the included studies: Determinants of mental health in graduate students are myriad; 2) Coping Strategies for Graduate Student Stress; and 3) Bridging Support: Enhancing Mental Well-Being. Conclusion: Our study's findings highlight the significance of investigating the mental well-being of graduate students in Canada. This review showed that by promoting mental well-being, universities and institutions can create a supportive atmosphere that encourages open dialogue, provides access to counseling and other mental health resources, and implements strategies to mitigate the challenges faced by graduate 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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.025 | 0.045 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".