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Record W2746337762 · doi:10.1177/1049732317725219

Surviving Surveillance: How Pregnant Women and Mothers Living With HIV Respond to Medical and Social Surveillance

2017· article· en· W2746337762 on OpenAlexafffund
Saara Greene, Allyson Ion, Gladys Kwaramba, Lisa Lazarus, Mona Loutfy

Bibliographic record

VenueQualitative Health Research · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of ManitobaSt. Michael's HospitalMaple Leaf Medical ClinicMcMaster UniversityWomen's College HospitalUniversity of Toronto
FundersOntario HIV Treatment Network
KeywordsHuman immunodeficiency virus (HIV)PregnancyDistancingMedicineStigma (botany)Qualitative researchFamily medicinePsychiatryCoronavirus disease 2019 (COVID-19)Sociology

Abstract

fetched live from OpenAlex

Pregnant women and mothers living with HIV are under surveillance of service providers, family members, and the community at large. Surveillance occurs throughout the medical management of their HIV during pregnancy, preventing HIV transmission to their baby, infant feeding practices, and as part of assessments related to their ability to mother. Enacted and anticipatory HIV-related stigma can exacerbate the negative impact that being under surveillance has on mothers living with HIV as they move through their pregnancy, birthing, and mothering experiences. In response, women living with HIV find ways to manage their experiences of surveillance through engaging in acts of distancing, planning, and resisting at different points in time, and sometimes enacting all three practices at once. Positioning the narratives of pregnant women and mothers living with HIV in relation to their experiences of surveillance illuminates the relationship between the surveillance of mothers living with HIV and HIV-related stigma.

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)0.029
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.187
GPT teacher head0.535
Teacher spread0.348 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2017
Admission routes2
Has abstractyes

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