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Record W2763912091 · doi:10.11591/ijphs.v6i3.7547

Knowledge, Attitude and Practice of Cervical Cancer Screening through Visual Inspection with Acetic Acid

2017· article· en· W2763912091 on OpenAlexaboutno aff
Susmita Kar, Md. Kapil Ahmed

Bibliographic record

VenueInternational Journal of Public Health Science (IJPHS) · 2017
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
Fundersnot available
KeywordsCervical cancerVisual inspectionMedicineQuarter (Canadian coin)Test (biology)Family medicineDiseaseCervical cancer screeningCancerCross-sectional studyEnvironmental healthPathologyInternal medicine

Abstract

fetched live from OpenAlex

Detection of the cervical cancer requires practice of screening that will increase survival rates from the disease. Visual Inspection with Acetic Acid (VIA) is an alternative to screening for cervical cancer.This study aims to assess the knowledge, attitude, and practice towards VIA screening among adult women. We used a cross-sectional study in urban areas of Bangladesh with a sample of 285 respondents those were interviewed through semi-structured questionnaire. Analyses have done by targeting the objectives and considering the indicators with appropriate test statsitsics.About 56.1% respondents aged between 18 and 35 years with majority had at least secondary education including 29.5% were from affluent group. Though the highest majority of women had the knowledge of cervical cancer but only quarter was aware of VIA. A few of the respondents adopted any screening test for detecting cervical cancer and this lower coverage could be due to, among other reasons, lack of knowledge, accessibility, and service availability. The results also indicated that higher educational level of respondents is the predictor of improving knowledge on the disease and early adoption of available test procedure.An awareness building program should be designed for women of early detection of cervical cancer using VIA procedure.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.194
GPT teacher head0.543
Teacher spread0.349 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Public Health Science (IJPHS)Same topicCervical Cancer and HPV ResearchFrench-language works237,207