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Enregistrement W7092500678 · doi:10.17605/osf.io/36khc

Integration and its relationship to mental health trajectories over time for Syrian refugees in Canada: A latent class growth analysis

2025· other· W7092500678 sur OpenAlexfundaboutno aff

Notice bibliographique

RevueOpen Science Framework · 2025
Typeother
Langue
DomainePsychology
ThématiqueMigration, Health and Trauma
Établissements canadiensnon disponible
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésRefugeeMental healthGovernment (linguistics)Settlement (finance)Private sectorDocumentationHealth care

Résumé

récupéré en direct d'OpenAlex

Resettled refugees arrive in Canada through three different programs - the Government-Assisted Refugees (GAR) program, where services and financial support are provided through Service Providing Organizations, the Private Sponsorship of Refugees (PSR) program, where support is provided by private citizens or not for profit groups, and the Blended Visa Office-Referred program (BVOR) where settlement support comes from private citizens but financial support is divided between government and private sponsors. Between November 2015 and July 2017, more than 31,000 Syrian refugees arrived to Canada through these different pathways, creating an opportunity to explore the longitudinal impact of integration support and pathways within a single cohort of refugee newcomers Before and during the migration journey many refugees experience severe hardships and traumatic life events including conflict-related violence, family loss, injuries and poor nutrition that put them at risk for both physical and mental health problems (Hansson et al., 2010; Pottie et al., 2011). The experience of resettlement is typically associated with little preparation or control over the destination or timing of migration, the need to leave material resources and documentation behind, and often following years of displacement and disrupted education and employment. Moreover, many resettled refugees have no family members or friends in the site of resettlement to assist in the settlement process. As a result, refugees can experience a range of resettlement challenges that are more pressing than those of other newcomers. Refugees’ health and integration experiences are tightly interwoven. Unmet physical and mental health needs create barriers to successful integration, including challenges finding and maintaining adequate employment, housing and education (Hynie, 2014). Simultaneously, growing evidence shows that existing physical and health problems may be exacerbated by social determinants of health including housing, employment, access to health and social services and experiences of social inclusion or exclusion (Hansson et al., 2010; Hynie, 2018). The need for societies to recognize and accommodate newcomers in order to support integration emphasizes the bilateral nature of integration; it is not merely a case of newcomers adapting to their new environments but environments adapting to meet the needs of all current members of the community (Hynie, 2024). The goal of this project was to document the impact of post-migration support and explore the ways in which social determinants influenced the long-term health and well-being of Syrians resettled to Canada during the major resettlement wave between 2015 and 2017. We conducted a mixed-methods longitudinal Canadian study (SyRIA.lth) that compared how GAR and PSR resettlement programs in three different provinces (Ontario, British Columbia and Quebec) support long-term social integration pathways for refugees and the impact of these pathways on physical and mental health. These participants were followed for four years, (Waves 1 through 4). Our goal was to recruit 10% of adult refugees who resettled in our six target cities. Ultimately, we successfully recruited 24.0% of adult GARs, 9.8% of adult PSRs and 13.8% of adult BVORs settled across the 6 cities, based on settlement statistics provided by Immigration, Refugees, and Citizenship Canada (IRCC). In the first annual wave of the study, 1932 base-line surveys with newly resettled Syrian refugees across Canada (922 from Ontario, 697 from Quebec and 313 from British Columbia) were completed of which 45% were GARs, 51% PSRs and 4% BVORs. In the Wave 2 follow-up survey, 1805 participants participated; 1716 in Wave 3, and 1665 in Wave 4. In addition, 153 participants participated in 20 focus groups nationally across Canada between year 1 and 2. These data have been analyzed and some of the early findings indicated that type of sponsorship and sociodemographic factors influenced integration outcomes of this population (Hynie et al., 2019), that mental health outcomes seemed to be getting worse over time (Ahmad et al., 2021), and that declining mental health may be particularly likely among moderately and highly educated newcomers who struggled to find employment commensurate with their education and experience (Bridekirk et al., 2021). The last year of planned data collection fell during 2020, 3 to 6 months into the first wave of pandemic, and demonstrated the early impact of COVID-19 restrictions on the health and well-being of the participants. During the pandemic, refugees also faced elevated risks of isolation due to limited digital literacy and lack of access to technology (Hynie et al., 2022; ISSofBC, 2021). The pandemic also exacerbated economic stress, and resulted in a rapid loss or reduction of employment, especially for those with lower levels of education, which can include refugees (Beland et al., 2020; IRCC, 2019). However, given that many of the sample were still transitioning to full-time employment and thus households were still relying on social assistance, or those working were working in "essential" sectors, the economic impact of the pandemic was less severe than it might have been for those who had been in the country longer. There are concerns that the data collected at that time did not adequately capture the long-term health and well-being of the sample, since it was collected in the midst of a crisis. It was also not clear if the impacts of, and factors that predict resilience to, the COVID pandemic could be determined so early into the pandemic. Therefore, in this present study we aim to conduct a final wave of data collection on a subset of participants to examine longer term integration outcomes and also explore the impact of the COVID-19 pandemic on the study sample. The project will produce knowledge that will inform promising practices for refugee integration and deepen our understanding of the influence of the social determinants of health (SDOH).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,638
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,007
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,026
Tête enseignante GPT0,369
Écart entre enseignants0,343 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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