Insights from a survey “comments” section: extending research on doctoral well-being
Bibliographic record
Abstract
Purpose The purpose of this study is to better understand the declines in doctoral students’ mental and physical health while pursuing their doctoral degrees, by revealing the major themes of students’ voluntary comments following a survey that primed students to reflect on these topics. Design/methodology/approach The present study used qualitative thematic analysis to uncover themes in doctoral students’ voluntary comments on a large-scale, web-based survey of graduate students’ motivation and well-being. Findings A thematic analysis revealed six major emerging themes: timing in the degree process, work-life balance, health/well-being changes, impostor syndrome, the supervisor and hopelessness. Research limitations/implications The themes uncovered in the present study contribute to the literature by highlighting important underexplored topics (e.g. timing in the degree process, hopelessness) in doctoral education research and they are discussed and situated in the context of existing literature. Practical implications Implications for doctoral supervisors and departments are discussed. Social implications The present study highlights some pressing concerns among doctoral students, as articulated by the students themselves and can contribute to the betterment of doctoral education, thereby reducing attrition, improving the experiences of doctoral students and possibly affording more candidates to achieve a doctoral degree. Originality/value The present study makes the above-mentioned contributions by taking a novel approach and analyzing doctoral students’ voluntary comments (n = 607) on a large-scale, web-based survey. Thus, while some of the themes were primed by the survey itself, the data represent issues/concerns that students perceived as important enough to comment about after already having completed a lengthy questionnaire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".