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Record W2803050037 · doi:10.1177/1049732318764394

Being and Becoming a Helper: Illness Disclosure and Identity Transformations among Indigenous People Living With HIV or AIDS in Saskatoon, Saskatchewan

2018· article· en· W2803050037 on OpenAlexafffundabout
Andrew R. Hatala, Kelley Bird‐Naytowhow, Tamara Pearl, Jen Peterson, Sugandhi del Canto, Eddie Rooke, Stryker Calvez, Ryan Meili, Michael Schwandt, Jason Mercredi, Patti Tait

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of SaskatchewanUniversity of Manitoba
FundersInstitute of Aboriginal Peoples Health
KeywordsIndigenousIdentity (music)Human immunodeficiency virus (HIV)Self-disclosurePsychologySociologyGender studiesMedicineSocial psychologyFamily medicine

Abstract

fetched live from OpenAlex

Saskatoon has nearly half of the diagnoses of HIV in Saskatchewan, Canada, with an incidence rate among Indigenous populations within inner-city contexts that is 3 times higher than national rates. Previous research does not adequately explore the relations between HIV vulnerabilities within these contexts and the experiences of illness disclosure that are informed by identity transformations, experiences of stigma, and social support. From an intersectionality framework and a constructivist grounded theory approach, this research involved in-depth, semistructured interviews with 21 Indigenous people living with HIV and/or AIDS in Saskatoon, both male and female. In this article, we present the key themes that emerged from the interviews relating to experiences of HIV disclosure, including experiences of and barriers to the disclosure process. In the end, we highlight the important identity transformation and role of being and becoming a "helper" in the community and how it can be seen as a potential support for effective community health interventions.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.099
GPT teacher head0.493
Teacher spread0.394 · 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 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

Citations13
Published2018
Admission routes3
Has abstractyes

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