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Record 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 on OpenAlexfundaboutno aff

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Language
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsRefugeeMental healthGovernment (linguistics)Settlement (finance)Private sectorDocumentationHealth care

Abstract

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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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.026
GPT teacher head0.369
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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