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Record W4296941139 · doi:10.2147/ahmt.s383872

Human Papillomavirus Vaccination Uptake and Its Predictors Among Female Adolescents in Gulu Municipality, Northern Uganda

2022· article· en· W4296941139 on OpenAlexaboutno aff
Caroline Aruho, Samuel Mugambe, Joseph Baruch Baluku, Ivan Mugisha Taremwa

Bibliographic record

VenueAdolescent Health Medicine and Therapeutics · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
Fundersnot available
KeywordsVaccinationHuman papillomavirusMedicineCervical cancerCross-sectional studyDemographyQuarter (Canadian coin)Descriptive statisticsGynecologyCancerVirologyGeographyInternal medicine

Abstract

fetched live from OpenAlex

Background: Human papillomavirus (HPV) is the putative case of cervical cancer. However, uptake of HPV vaccination is reportedly low in Uganda. This study explored the predictors of HPV vaccination uptake among female adolescents aged 15-18 years in Gulu Municipality, in northern Uganda. Methods: This was an analytical cross-sectional survey that was conducted among adolescents aged 15-18 years in Gulu Municipality. A structured questionnaire was used. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 25. Descriptive statistics and a log binomial model were used to analyze the factors associated with HPV vaccination uptake. Results: Less than a quarter of the female adolescents (22%) aged 15-18 years in Gulu municipality, Gulu district, had been vaccinated with the human papillomavirus vaccine. HPV vaccination uptake was lower by 23% among adolescents who stayed with their mothers only (aPR = 0.769, CI = 0.595-0.995, P = 0.046), and by 14% among adolescents whose parents were unmarried (aPR 0.859, CI = 0.776-0.951, P=0.003). Conclusion: This study reports a low HPV vaccination coverage among adolescents in Gulu Municipality, which is associated with parental perceptions and marital status. Efforts to increase uptake should focus on parents of adolescents.

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 score0.094
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.099
GPT teacher head0.401
Teacher spread0.302 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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