Examining graduate student perspectives on supervision and peer mentoring across four professional faculties
Bibliographic record
Abstract
Purpose The purpose of the study was to examine graduate student perspectives on the common and unique roles peer mentors and supervisors play in supporting student success and wellbeing during their program. Design/methodology/approach A qualitative research design involving semistructured interviews with 62 thesis-based masters and doctoral students from four professional faculties, Education, Medicine, Nursing and Social Work, at a large public research-intensive university in Canada. Findings Findings transcend the four disciplines of study. Communities of support are described that involve both supervisors and peers in combination, clusters of meaning by supervisory paradigm are identified and original findings presented that expand upon the learning alliance framework by explicitly considering the role of peer mentors in graduate student success. Research limitations/implications While supervisors bear primary responsibility for fostering effective research-based relationships, this study’s findings strengthen the argument that mentoring and advising of graduate students is most effective when conducted within a collaborative community of support that involves learning alliances among faculty, peers, program staff and academic leaders across the institution. Practical implications A four-pronged approach to graduate education that emphasizes the collective responsibility of institutions, programs, supervisors and students in creating a supportive ecosystem for holistic graduate student academic success and wellbeing is recommended. Social implications Key argument that it is essential to embrace a collaborative and community of support mindset, where multiple stakeholders actively contribute to the wellbeing and academic development of graduate students throughout their programs. Originality/value A cross-disciplinary perspective is offered on the importance of both supervisors and peers in assisting thesis-based graduate students to successfully navigate academic, social and personal journeys through graduate school.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".