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Assessment of Menopausal Women Performance regarding Vulvar Self Examination

2025· article· en· W4415347782 on OpenAlexaboutno aff
Rabab Mohamed Hussien

Bibliographic record

VenueHelwan International Journal of Nursing Research and Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsVulvaMenopauseQuarter (Canadian coin)Postmenopausal womenGynecological ExaminationObstetrics and gynaecologyVulvar Diseases

Abstract

fetched live from OpenAlex

Background: Vulvar self-examination can help the women to discover the presence of any abnormality and important to recognize any physical change. Aim: To assess of menopausal women performance regarding vulvar self-examination. Design: A descriptive research design was used to conduct the study. Setting: The study was conducted at obstetrics and gynecological clinic at Arab Contractors Medical Center. Sample: A purposive sample of 110 menopausal women who attended at Arab Contractors Medical Center. Tools of Data Collection: Tool I; Knowledge assessment questionnaire, to assess menopausal women knowledge regarding vulvar self-examination. Tool II; Practical self-assessment checklist, to assess Menopausal women’s practice regarding vulvar self- examination. Results: More than three quarter of menopausal women had poor knowledge, and less than one quarter had average and good knowledge about vulvar self-examination. In addition, more than half of the menopausal women had examined their vulva incorrectly and less than one quarter of menopausal women had examined their vulva correctly. There was a strong positive correlation between women's knowledge and their practice regarding vulvar self-examination. Conclusion: Vulvar self-examination can help in early detection of any abnormalities and no delay to become severely. Recommendations: Design educational program to raise menopausal women awareness regarding to the symptoms required to perform vulvar self-examination

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)0.006
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.129
GPT teacher head0.534
Teacher spread0.405 · 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 designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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