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Record W4225592385 · doi:10.1155/2022/5340064

Identification Level of Awareness and Knowledge of Emirati Men about HPV

2022· article· en· W4225592385 on OpenAlexfundno aff
Suzan Al Shdefat, Shamsa Al Awar, Nawal Osman, Howaida Khair, Gehan Sallam, Sara Maki

Post-publication record

OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.

Bibliographic record

VenueJournal of Healthcare Engineering · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
FundersHashemite UniversityUniversity of JordanConcordia University
KeywordsCervical cancerVaccinationHuman papillomavirusFamily medicineMedicineHPV vaccinesHPV infectionCancerGynecologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

One of the most frequent cancers that affects males globally is cervical cancer (CC) that kills hundreds or even thousands of women each year, particularly in underdeveloped nations. The study focuses on human papillomavirus (HPV) that contributes to cervical cancer (CC) development. In the majority of Arab nations, there seems to be no public education or vaccination programs. In research, methodological rigor is employed to find solutions to both theoretical and practical difficulties. This research aims to assess the knowledge and awareness of the HPV vaccination among Emirati men. Results of the research showed that Emirati males had a poor understanding of HPV and its vaccination. According to the findings of this research, Emirati males lack a basic understanding of HPV, which necessitates the implementation of national HPV education initiatives. We have identified several critical knowledge gaps that can be filled in the future regarding HPV infection and vaccination.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.103
GPT teacher head0.400
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2022
Admission routes1
Has abstractyes

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