Exploring professors’ experiences supporting graduate student well-being in Ontario faculties of education
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore Ontario education professors’ perceptions of well-being, document ways in which they support graduate students’ well-being and discuss perceived challenges in doing so. Design/methodology/approach A basic interpretative design was used, with participants consisting of seven (four females, three males) tenured professors from five faculties of education in Ontario, Canada. Participants completed one to two semi-structured interviews. Interviews were audio recorded, transcribed for member checking and read holistically to identify emergent themes across participants. Findings Participants provided multifaceted representations of well-being and reported that supporting graduate students’ psycho-socio-emotional well-being was a critical aspect of their role. They discussed the intentional use of specific strategies including creating inclusive learning environments, nurturing caring relationships, providing academic accommodations and promoting relevant on-campus supports and services. Finally, participants identified factors that challenged their abilities to support graduate students’ wellness including institutional norms and expectations, shifting student demographics and uncertainties with respect to professional capacities. Practical implications Graduate student mentorship should be included in the faculty reward system. The provision of private, specialized services offered by trained personnel is also recommended. Future research is needed to explore faculty experiences supporting and mentoring diverse groups of graduate students. Originality/value While limited in participant numbers and educational jurisdiction, this research extends current mentoring models by adding a mental health and well-being component, thus bridging gaps between well-being and graduate mentorship in higher education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".