The Immigration and Settlement Experiences of International Students From Ghana and Nigeria in the Greater Toronto Area
Notice bibliographique
Résumé
Over the last 20 years, the number of international students coming to Canada has increased drastically from just over 100,000 in 2000-2001 to well over half a million in the 20202021 academic years (Canadian Bureau for International Education (CBIE), 2018; El-Assal, 2020; Statista, 2021). According to Global Affairs Canada (2020), in 2018 alone, international students in Canada contributed $22.3 billion to the Canadian economy on tuition, accommodation, and discretionary spending and helped to sustain over 170,000 jobs. Much of the literature has focused on Asian international students – especially from India and China, who make up a combined 56% of the international student body in Canada (El-Assal, 2020). Significantly less is known about African international students, who make up about 9% of the international student body in Canada. This study critically assesses Canada’s approach to and focus on international students as part of overall programs to attract immigrants. Using a qualitative analysis, this study focuses on the immigration and settlement experiences of Black African international students from Ghana and Nigeria living in the Greater Toronto Area (GTA) Canada. Semi structured interviews were conducted with Ghanaian (N = 6) and Nigerian (N = 14) international students living in the GTA. Because experiences may defer between male and female, an even number of students who identified as men (N = 10) and identified as women (N = 10) were recruited. The study centred the voices of these Ghanaian and Nigerian international students, adding to the scarce body of knowledge surrounding the topic. The research focused on the ways in which social networks contributed to or diminished acculturative stress; the impact of Canadian immigration policy on the experiences of these students; and the role of race and gender on the immigration experiences of these students. The study resulted in three key finding. First, Ghanaian and Nigerian international students face financial difficulties. They could benefit from additional forms of financial aid, grants, or financial support, although participants identified as female experienced more financial challenges than those identified as male. Second, there is a lack of culturally appropriate support from colleges and universities for Ghanaian and Nigerian international students, which have led some to seek support from their religious institutions, some of which are not equipped to help with mental wellbeing. Lastly, some participants that used educational agents, expressed discontent and frustration with the outcomes, as agencies at times gave them misinformation that led them to enroll in bridging programs when, in fact, this was not necessary. The study provides recommendations that can inform policy change to better support and enhance the experiences of Ghanaian and Nigerian international students in the GTA.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,017 | 0,008 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».