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Record W1971750436 · doi:10.2989/16085906.2015.1016985

Experiences of work among people with disabilities who are HIV-positive in Zambia

2015· article· en· W1971750436 on OpenAlexafffund
Janet Njelesani, Stephanie Nixon, Debra Cameron, Janet Parsons, J. Anitha Menon

Bibliographic record

VenueAfrican Journal of AIDS Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsSt. Michael's HospitalCentre for Disability Prevention and RehabilitationUniversity of Toronto
FundersCanadian Institutes of Health ResearchInyuvesi Yakwazulu-Natali
KeywordsStigma (botany)Human immunodeficiency virus (HIV)Thematic analysisMedicineSocial stigmaWork (physics)Qualitative researchGerontologyPsychologyPsychiatrySociologyFamily medicineSocial science

Abstract

fetched live from OpenAlex

This paper focuses on accounts of how having a disability and being HIV-positive influences experiences of work among 21 people (12 women, 9 men) in Lusaka, Zambia. In-depth semi-structured interviews were conducted in English, Bemba, Nyanja, or Zambian sign language. Descriptive and thematic analyses were conducted. Three major themes were generated. The first, a triple burden, describes the burden of having a disability, being HIV-positive, and being unemployed. The second theme, disability and HIV is not inability, describes participants' desire for work and their resistance to being regarded as objects of charity. Finally, how work influences HIV management, describes the practicalities of working and living with HIV. Together these themes highlight the limited options available to persons with disabilities with HIV in Lusaka, not only secondary to the effects of HIV influencing their physical capacity to work, but also because of the attendant social stigma of being a person with a disability and HIV-positive.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.059
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.389
Teacher spread0.308 · 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 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

Citations6
Published2015
Admission routes2
Has abstractyes

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