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Record W4384119428 · doi:10.1093/heapro/daad073

Determinants of cervical cancer screening among women living with HIV in Zimbabwe

2023· article· en· W4384119428 on OpenAlexaff
Roger Antabe, Nasong A. Luginaah, Joseph Kangmennaang, Paul Mkandawire

Bibliographic record

VenueHealth Promotion International · 2023
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsQueen's UniversityWestern UniversityCarleton UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineResidenceCervical cancerHuman immunodeficiency virus (HIV)DemographyCervical cancer screeningEnvironmental healthGerontologyCancerImmunologyInternal medicine

Abstract

fetched live from OpenAlex

In sub-Saharan Africa (SSA), cervical cancer (CC) is the second leading cause of cancer-related deaths, with human immunodeficiency virus (HIV) seropositive women being particularly vulnerable. Despite the benefits of early CC screening in reducing HIV-related CC deaths, CC screening uptake remains limited, with wide disparities in access across SSA. As part of a larger study, this paper examines the determinants of CC screening among HIV-seropositive women of reproductive age (15-49 years) in Zimbabwe. Using the 2015 Zimbabwe Demographic and Health Survey, we conducted multilevel analyses of CC screening among 1490 HIV-seropositive women, nested in 400 clusters. Our findings revealed that, even though 74% of HIV-seropositive women knew about CC, only 17.6% of them reported ever screening for it. Women who held misconceptions about HIV (OR = 0.47, p = 0.01) were less likely to screen for CC compared to those with accurate knowledge about HIV and CC. HIV-seropositive women with secondary or higher education were more likely to screen (OR = 1.39, p = 0.04) for CC compared to those with a primary or lower level of education. Age was positively associated with screening for CC. Furthermore, locational factors, including province and rural-urban residence, were associated with CC screening. Based on these findings, we call for integrated care and management of HIV and non-communicable diseases in Southern Africa, specifically, Zimbabwe due to the legacy of HIV in the region.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.997

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)0.0040.000

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 head0.070
GPT teacher head0.423
Teacher spread0.352 · 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 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

Citations8
Published2023
Admission routes1
Has abstractyes

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