An exploration of graduate student peer mentorship, social connectedness and well-being across four disciplines of study
Bibliographic record
Abstract
Purpose Student mental well-being is a matter of increasing concern on university campuses around the world. Social, psychological, academic and career aspects of graduate learning are enriched through peer mentorship. Peer-mentoring experiences and the impacts of these relationships on the mental well-being of graduate students remain underexplored in the scholarship of teaching and learning. The purpose of this study was to explore how engagement in formal and informal peer mentorship, as described by students across four academic disciplines, impacts the social connectedness and well-being of graduate students. Design/methodology/approach A convergent mixed methods research design was used, with quantitative and qualitative data gathered in parallel to gain a comprehensive, corroborated and integrated understanding of graduate students’ perspectives and experiences with peer mentorship. Online survey and interview data were collected from graduate thesis-based master’s EdD and PhD students in education, medicine, nursing and social work. The data were analyzed using descriptive statistics and thematic analysis. Findings The authors found a commonality of graduate student experiences across disciplines with respect to the diverse psychosocial impacts of graduate peer mentorship. Peer-mentoring relationships offered mentees emotional support, motivation and a sense of community and offered mentors opportunities for self-development and gratification. Originality/value This research is unique in its in-depth exploration of the interdisciplinary perspectives and experiences of graduate students from Education, Nursing, Medicine and Social Work. While further research is needed to explore the implementation of structural approaches to support the development of peer-mentoring relationships in graduate education, the multidisciplinary focus and depth and breadth of this inquiry suggest the potential transferability of the study findings to other disciplines and academic settings. The findings from this study further highlight the need for strategic activation of existing program resources to foster greater connectedness and well-being among graduate students.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 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".