The Wind Beneath Their Wings? Faculty Support for Students with Mental Ill-Health at an Ontario University
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,015 | 0,005 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».