Falling Through the Cracks: Graduate Students’ Experiences of Mentoring Absence
Bibliographic record
Abstract
Theory about how mentorship is supposed to work, its goals, and what “makes it work” is abundant. It is rarer to encounter empirical information about processes and implications when mentoring is a problem. Graduate students experience significant psychological, physical, financial, and relational challenges and likely have expectations about the ameliorating effects of mentorship. Limited qualitative literature has described graduate students’ perceptions about problems with mentorship and students’ outcomes. The descriptive qualitative study used secondary analysis to describe students’ perceptions of problematic mentorship. Graduate student research assistants conducted 12 recorded focus group interviews and transcribed them. The authors used inductive content analysis to develop themes. Fifty-four participants were recruited, including masters’ (n=19), PhD (n=34), and a graduate student not enrolled in a specific faculty (n=1). The students represented multiple disciplines. The major theme identified was “falling through the cracks.” The subthemes included missing mentorship, students’ difficulties accessing mentorship, university structures undermining mentoring, and damage to mentees. Falling through the cracks highlights students’ struggles with accessing mentorship, effects of missing mentorship, and students’ solutions for modifying structural features that inhibit mentoring. Quantitative work could compare psychological outcomes associated with present and missing mentoring.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".