Exclusionary Structures: A Multi-Method Analysis of Structural Barriers Against University Students with Mental Health Challenges
Notice bibliographique
Résumé
In dominant Canadian culture presently, it is taken for granted that “psy” professionals (e.g., counsellors, psychologists, psychiatrists) play a central role in the lives of individuals with mental health challenges. Indeed, much of the knowledge about mental illness is created by such professionals, and focuses on treatment and recovery. This focus has been costly, as it situates suffering within the individual, and ignores structural determinants of well-being. This results in structures that are exclusionary and discriminatory towards individuals with mental health challenges, which in turn makes it challenging for such individuals to achieve positions of power to influence knowledge production and systems. Although many forms of stigma exist, structural stigma refers to the policies of institutions and cultural norms within a society that intentionally or unintentionally limit individuals with mental health challenges’ access to various rights, resources and opportunities. In this dissertation, I examined the presence of structural stigma towards individuals with mental health challenges at the University of Victoria in two studies. I used participatory practices, by having current and former University of Victoria students with mental health challenges as members of the research team throughout. In Study 1, current and former University of Victoria students (n = 275) completed a survey of structural barriers they had encountered, and reported on solutions and supports that were helpful. Seven dimensions of barriers were identified: 1) barriers in mental health care, 2) stigma and negative interpersonal interactions, 3) navigation of services barriers, 4) practical support knowledge barriers, 5) financial barriers, 6) learning barriers, and 7) inappropriate mental health services. Four dimensions of barriers specific to University of Victoria’s Centre for Accessible Learning (CAL) were also identified: 1) helpfulness of CAL services, 2) misfit of CAL services, 3) disclosure-related barriers, and 4) CAL administrative barriers. Upon follow-up analyses, these barriers were inequitably distributed, disproportionately impacting marginalized students in various ways. Study 2 consisted of a multi-part World Café focused on barriers related to self-advocacy. Current and former University of Victoria students (n = 21) discussed experiences of self-advocacy and solutions that could improve these barriers in rotating groups. I analyzed the data using thematic analysis, and identified three themes: 1) the structural context of self-advocacy, 2) the relational context of self-advocacy, and 3) rejecting self-advocacy. An additional discussion of short-term recommendations from participants is provided. To close, I reflect on the execution of participatory practices within this dissertation. I also discuss the implications of these results for broader anti-stigma agendas, and argue for community-centered approaches to supporting students with mental health challenges at university. Finally, I discuss the complexities and possibilities of taking action to better support students with mental health challenges at university.
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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 | 0,043 | 0,068 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».