Facing the dropout crisis among PhD candidates: the role of supervisor support in emotional well-being and intended doctoral persistence among men and women
Bibliographic record
Abstract
The number of PhD candidates who experience psychological problems has risen significantly over the past few years. Poor mental health can have numerous negative consequences for PhD candidates and their supervisors, as it may adversely affect their quality of life, attrition, and academic productivity. Despite these well-documented challenges, few studies have looked at how the supervisor – supervisee relationship can influence the emotional well-being of male and female doctoral candidates. The current work examined the role of the supervisor’s support in emotions and intended doctoral persistence among men (n = 411) and women (n = 514), in all disciplines at two large universities in Belgium. Results indicate that emotional well-being was low for all doctoral candidates but women experienced even more negative emotions (anxiety, stress, discouragement, demoralization, sadness and depression) and fewer positive emotions (confidence, optimism, happiness, fulfillment, satisfaction and content) than men. Interestingly, we also found that perceived structure and autonomy, two dimensions of supervisor support, have a positive effect on emotional well-being and intention of pursuing a PhD trajectory for both men and women. This paper makes a contribution to the higher education and research supervision literature by offering new directions for research and by providing guidelines for the training of research supervisors.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".