Perceived stress and well-being in doctoral students: effects on program satisfaction and intention to quit
Bibliographic record
Abstract
Stress is a common negative emotion in students. Given the stress associated with doctoral studies, it is crucial to examine the influence of stress on well-being, program satisfaction, and retention in doctoral programs. This study examined stress-related issues and their relationships with intention to quit in a sample of 2,486 students enrolled in doctoral programs representing 38 disciplines. Participants completed a web-based questionnaire including sociodemographic and self-report measures assessing perceived stress, emotional, social, and psychological well-being, as well as program satisfaction and intention to quit. We tested three hypotheses based on Lazarus and Folkman’s transactional theory of stress and emotions and Núñez-Regueiro’s stress process model of school dropout. The results of Structural Equation Modeling (SEM) supported the hypotheses of the study and showed that perceived stress is negatively associated with emotional, social, and psychological well-being. The most significant finding from this study is that perceived stress, directly and indirectly contributes to lower program satisfaction in doctoral students and a stronger intention to quit. These study findings underscore the need for departments to actively support students in completing their dissertations by establishing explicit expectation norms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".