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Record W4210596366 · doi:10.3390/curroncol29020047

Cervical Cancer Prevention and High-Risk HPV Self-Sampling Awareness and Acceptability among Women Living with HIV: A Qualitative Investigation from the Patients’ and Providers’ Perspectives

2022· article· en· W4210596366 on OpenAlexvenueno aff
Daisy Le, Annie Coriolan Ciceron, Min Jeong Jeon, Laura Isabel Gonzalez, Jeanne A. Jordan, José Bordón, Beverly Long

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesGeorge Washington University
KeywordsMedicineCervical cancerPsychological interventionCervical cancer screeningFamily medicineSampling (signal processing)Cervical screeningCancerGynecologyNursingInternal medicine

Abstract

fetched live from OpenAlex

Routine cervical cancer screening is important for women living with HIV (WLH) due to the greater incidence and persistence of high-risk HPV (HR-HPV) infection. HR-HPV self-sampling has been proposed to overcome barriers to in-office cervical cancer screening in underserved populations. However, little is known about baseline knowledge of HR-HPV and the acceptability of HR-HPV self-sampling among WLH. This paper describes WLH's experiences and needs regarding cervical cancer screening, specifically HR-HPV self-sampling, and seeks to reconcile their experiences with the views of their providers. In total, 10 providers and 39 WLH participated in semi-structured interviews and group discussions, respectively. Knowledge of cervical cancer and HR-HPV was generally limited among WLH; when present, it was often due to personal experience of or proximity to someone affected by cervical cancer. Most WLH were not familiar with HR-HPV self-sampling but, despite some of the providers' skepticism, expressed their willingness to participate in a mail-based HR-HPV self-sampling intervention and highlighted convenience, ease of use, and affordability as facilitators to the uptake of HR-HPV self-sampling. The experiences identified can be used to guide patient-centered communication aimed at improving cervical cancer knowledge and to inform interventions, such as HR-HPV self-sampling, designed to increase cervical cancer screening among under-screened WLH.

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 score0.105
Threshold uncertainty score0.955

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.422
Teacher spread0.336 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

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