The Wind Beneath Their Wings? Faculty Support for Students with Mental Ill-Health at an Ontario University
Bibliographic record
Abstract
The recent increase in enrollment of students with mental ill-health in universities has been described as an epidemic. This has led to much research into student mental health and how to support it. Little of this research, though, has focused on faculty instructors and their role in supporting these students. The purpose of this study was to explore this role and to examine the factors that affect how and whether faculty members support students with mental ill-health in their classes. Participants were 17 faculty members and 5 expert informants from one large university in Southern Ontario. Faculty members were interviewed about their experiences with students with mental ill-health in their classes and the results were analyzed using Lipsky’s Street-Level Bureaucracy framework as well as via common themes found in interviewees’ responses. Findings revealed an important gender gap between faculty members when it came to role definition and perception as well as workload concerns and whether or not they believed students who disclosed mental health difficulties to them. Findings also showed that most faculty members considered their knowledge and qualifications to support these students as poor, which often related to a perception of inadequate professional development and training. Faculty members also expressed anxiety around issues regarding student accommodations due to concerns over academic integrity and fairness to all students. Findings also showed that faculty members tend to approach local actors for help, such as colleagues and department heads, rather than institutional actors such as Student Counselling or Student Accessibility Services. This latter finding has important implications for how and where universities should support faculty members who work with students with mental ill-health. Further studies are encouraged to focus on the role of the faculty instructor in supporting this cohort of students, as well as on how such support is enacted and what type of support is most helpful to students. Including faculty instructors in a holistic system of student support will go a long way towards providing a more suitable academic environment for students with mental ill-health on campus.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".