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Record W4225630248 · doi:10.1177/10497323221082958

Legislatively Excluded, Medically Uninsured and Structurally Violated: The Social Organization of HIV Healthcare for African, Caribbean and Black Immigrants with Precarious Immigration Status in Toronto, Canada

2022· article· en· W4225630248 on OpenAlexafffundabout
Apondi J. Odhiambo, Lisa Forman, LaRon E. Nelson, Patricia O’Campo, Daniel Grace

Bibliographic record

VenueQualitative Health Research · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Toronto
FundersNational Institute of Mental HealthCanadian Institutes of Health ResearchOntario HIV Treatment Network
KeywordsImmigrationHealth careLegislationLegislaturePolitical scienceEconomic growthEquity (law)Health equitySociologyLawEconomics

Abstract

fetched live from OpenAlex

African, Caribbean and Black immigrants face persistent legislative barriers to accessing healthcare services in Canada. This Institutional Ethnography examines how structural violence and exclusionary legislative frameworks restrict the right to HIV healthcare access for many Black immigrants. We conducted semi-structured interviews with Black immigrants living with HIV ( n = 20) and healthcare workers in Toronto, Canada ( n = 15), and analyzed relevant policy texts. Findings revealed that exclusionary immigration and healthcare legislation shaping and regulating immigrants’ right to health restricted access to public resources, including health insurance and HIV healthcare and related services, subjecting Black immigrants with precarious status to structural violence. Healthcare providers and administrative staff worked as healthcare gatekeepers. These barriers undermine public health efforts of advancing health equity and ending HIV “while leaving no one behind.” We urge continued policy reforms in Canada’s immigration and healthcare systems regarding HIV care access for Canada’s precarious status immigrants.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.458
Teacher spread0.369 · 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.

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

Citations19
Published2022
Admission routes3
Has abstractyes

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