Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how doctoral students experienced mentorship in their supervision context and how the mentorship they received impacted their well-being. Design/methodology/approach An interpretive phenomenological methodology was selected to frame the research design. This research approach seeks to study the individual lived experience by exploring, describing and analyzing its meaning. Findings The findings revealed three different quality levels of mentorship in this context authentic mentorship, average mentorship and below average/toxic mentorship. Doctoral students who enjoyed authentic mentorship experiences were more motivated and satisfied, students who reported average mentorships needed more attention and time from their supervisors, and students who had below average/toxic mentorships were stressed out and depleted. Research limitations/implications A limitation of this study is the lack of generalizability owing to the small sample size typical in qualitative studies. Another limitation is that this research did not include students who quit their programs because of dysfunctional supervision experiences. Practical implications Students and supervisors can use the findings to reflect on their beliefs and practices to evaluate and improve their performances. Also, authentic mentors can benefit from the findings to create a positive culture for all students to receive support. Finally, current supervisory policies can be reviewed in light of this paper’s findings. Social implications The findings show the nature of mentorship in an authoritative context, and how it can be toxic when power is misused. Originality/value This study provides new knowledge in relation to the different types of mentorship experiences that exist in doctoral supervision, and how each type can influence students’ well-being differently. Additionally, it reveals that doctoral students can graduate, even in the face of toxic mentorship, but at the expense of their well-being.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".