Time to act: Mental health and desired university supports among graduate students
Bibliographic record
Abstract
Research on university student well-being often fails to differentiate between graduate and undergraduate students or overlooks graduate students entirely. The different characteristics of graduate (vs. undergraduate) students may contribute to unique mental health needs in this population; hence research is needed to specifically address their psychological functioning. Thus, the goals of the present study were to report on: (1) the mental health profile of a large sample of Canadian graduate students; and (2) graduate students’ recommendations for what their university could do to improve their well-being. We used an online survey administered from September 2021 to October 2022. Participants included 648 graduate students from a university in Southwestern Ontario, Canada (response rate = 20.6%; Mage = 27.9; 73.1% women; 65.1% White). We examined mental health variables and perceived university supportiveness; and conducted an inductive content analysis of recommendations to improve university-provided support. Graduate students reported alarming rates of ill-being across various domains. Advisor support was associated with better graduate student mental health across multiple indicators. Most frequently, graduate students requested increased financial support to improve well-being. Taken together, these results punctuate the need for action to support graduate students and provide suggestions for meaningful change.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".