Effects of a Participation in a Structured Writing Retreat on Doctoral Mental Health: An Experimental and Comprehensive Study
Bibliographic record
Abstract
Challenges faced by doctoral researchers led to a concerning “doctoral mental health crisis” within academia. Recognizing the pressing need to address mental health concerns, notably among doctoral students, the Quebec Ministry of Higher Education introduced the Higher Education Student Mental Health Action Plan 2021–2026. One potentially relevant intervention approach is the implementation of tailored structured writing retreats for graduate students. Aiming to measure and explain the effects of participating to a three-day writing retreat on doctoral mental health, this study followed an explanatory sequential mixed method, including an experimental design. One hundred doctoral researchers were randomly assigned to either the experimental group (n = 50) or the waitlist control trial group (n = 50). Both groups answered a questionnaire comprising validated scales and open-ended questions at different timepoints, separated by a two-week gap. Results reveal that writing retreats reduced doctoral researchers’ psychological distress and improved their psychological, emotional, and social wellbeing. Among the multiple writing retreat aspects evaluated, only productivity experienced, as well as socialization/networking opportunities, acted as predictors for all doctoral mental health measures. Qualitative findings further supported the importance of perceived productivity and socialization/networking in promoting doctoral mental health. Recommendations are provided for fostering a supportive research work environment for doctoral researchers.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".