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Record W2494890292 · doi:10.1016/j.jana.2016.07.003

Stigma Experiences in Marginalized People Living With HIV Seeking Health Services and Resources in Canada

2016· article· en· W2494890292 on OpenAlexafffundabout
Leeann Donnelly, Lauren Bailey, Abbas Jessani, Jonathan Postnikoff, Paul Kerston, Mario Brondani

Bibliographic record

VenueJournal of the Association of Nurses in AIDS Care · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsPositive Living Society of British ColumbiaBamfield Marine Sciences CentreUniversity of British Columbia
FundersVancouver Foundation
KeywordsStigma (botany)Thematic analysisFocus groupHuman immunodeficiency virus (HIV)Citizen journalismHealth careMedicinePsychologyQualitative researchSociologyPolitical scienceFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

HIV stigma may prevent people from obtaining a timely diagnosis and engaging in life-saving care. It may also prevent those who are HIV infected from seeking health and education resources, particularly if they are from marginalized communities. We inductively explored the roots of stigma and its impact on health services and resource seeking as experienced by HIV-infected members of marginalized communities in Vancouver, British Columbia, Canada, using a community-based participatory research framework. Five peer-facilitated focus groups were conducted with 33 Aboriginal, Latino, Asian, and African participants. Thematic analysis of the experiences revealed four dominant themes: beginnings of stigma, tensions related to disclosure, experiences of service seeking, and beyond HIV stigma and discrimination. Persons living with HIV from Aboriginal and refugee communities continue to experience disproportionate rates of stigma and discrimination. Fear remains a prime obstacle influencing these groups' abilities and willingness to access care in various settings.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.286
Teacher spread0.278 · 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 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

Citations60
Published2016
Admission routes3
Has abstractyes

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