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Record W4409864447 · doi:10.1186/s12889-025-22673-9

Impacts of local, provincial, and federal immigration policies on health and social services access among women with precarious immigration status

2025· article· en· W4409864447 on OpenAlexafffundabout
Hanah Damot, Shaina Schafers, Mei-ling Wiedmeyer, Stefanie Machado, Elmira Tayyar, Padmini Thakore, Ruth Lavergne, Shira M. Goldenberg

Bibliographic record

VenueBMC Public Health · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsSt. Paul's HospitalDalhousie UniversitySimon Fraser UniversityMcMaster UniversityUniversity of British Columbia
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchMichael Smith Health Research BCVancouver Foundation
KeywordsImmigrationDeportationMedicineHealth carePublic healthRefugeeImmigration policySocial isolationSocial WelfareEconomic growthEnvironmental healthPolitical scienceNursingLaw

Abstract

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OBJECTIVES: Im/migrant women (e.g., non-status immigrants, refugee claimants, students, temporary foreign workers, visitors, and other migrants) face structural barriers to health and social services access. While immigration is an increasingly recognized social determinant of health, there remains a gap in literature on how structural determinants such as immigration policies and practices (e.g., 'status-checking', immigration status) shape im/migrant women's experiences navigating health and social services. This study aimed to examine the ways in which local, provincial, and federal immigration policies shape health and social services access among im/migrant women with precarious status. METHODS: Between December 2018 and February 2020, we conducted and thematically analyzed qualitative in-depth interviews with im/migrant women (N = 51), and service providers (N = 10) across Metro Vancouver. Data were collected as part of the IRIS study, which is a community-based, mixed-methods study of im/migrants' healthcare access prior to and during the COVID-19 pandemic. RESULTS: Despite policies that purportedly aim to grant access to health and social services in Vancouver regardless of immigration status, participants routinely described ineligibility and fear of detention and/or deportation as pervasive barriers to accessing services, including routine, preventive, and emergency health services, and enrolment of children in schools. Women described social isolation and exclusion as key consequences of federal immigration policies that produced precariousness through temporary and undocumented status. Overall, participants recommended for the elimination of immigration law enforcements and 'status-checking' practices in health and social settings. CONCLUSION: Sanctuary City policies are recommended to advance im/migrants' human rights, reduce instances of delayed or denied care, untreated illnesses, and social isolation. Full implementation of Sanctuary principles at the local level (i.e., reduced collaboration between local service providers and federal immigration enforcement) is needed to improve access to health and other services based on need, regardless of immigration status. At the provincial level, elimination of 'status checking' in health settings and expansion of eligibility criteria for health, social, and education programs (e.g., Medical Services Plan, subsidized housing, and BC's School Act) to include all im/migrants should be considered. At the federal level, increased funding for programs that address inequities in health and social services produced by restrictive immigration policies and ensure pathways to more secure immigration status are recommended. Together, these policy reforms have the potential to address the structural barriers to im/migrant women's health and social services access, and ultimately improve overall public health outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.807

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
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 head0.023
GPT teacher head0.352
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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