Mental Health in Doctoral Students: Investigating the Role of Supervisor Support
Bibliographic record
Abstract
While it is well-known in research on doctoral education that there remains a 50% attrition rate, the research on doctoral students’ well-being and emotions remains underexplored. Specifically, there is a lack of quantitative research examining the role of supervisory support of doctoral students’ emotions and psychological well-being. Doctoral-level students (N = 636) recruited internationally from a total of 36 countries across 41 disciplines. Participants completed an online questionnaire consisting of several self-report measures including supervisor support (i.e., availability, personal, autonomy, and academic support), emotional well-being, and global psychological adjustment (burnout, depression, intention to quit, impostor syndrome). Hierarchical regression analyses controlling for the effect of age, gender, discipline, Ph.D. year and Ph.D. stage showed significant beneficial effects of supervisor support to higher levels of positive emotions and lower levels of negative emotions, with students receiving better support from their supervisor also reporting lower levels of intention to quit, burnout, depression, and impostor syndrome. Results highlighted the importance of the supervisory relationship in the experiences of doctoral students within their programs by showing higher supervisor support to predict better mental health indicators and higher quality of life in doctoral students, thus contributing to a decreased attrition rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".