MétaCan
Menu
Back to cohort
Record W2246738330 · doi:10.1080/13548506.2015.1093646

Age and sex or gender (sex/gender) and HIV vaccine preparedness

2015· article· en· W2246738330 on OpenAlexaff
Shayesta Dhalla

Bibliographic record

VenuePsychology Health & Medicine · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of British Columbia
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPreparednessContext (archaeology)DemographyHIV vaccineMen who have sex with menHuman immunodeficiency virus (HIV)MedicineVaccine trialImmunologyGeographyPolitical scienceSociologySyphilis

Abstract

fetched live from OpenAlex

An examination of age and sex or gender (sex/gender) in HIV vaccine preparedness studies can contribute to an understanding of these demographic variables in preparation for actual HIV vaccine trials. In this descriptive review, age and sex or gender (sex/gender) were examined in relation to willingness to participate (WTP) and retention in an HIV vaccine trial. Twenty-five articles were retrieved from the Organization for Economic Co-operation and Development (OECD) countries and 28 articles were retrieved from the non-OECD countries. In US studies that involved mainly white MSM, older men were more likely to be WTP in a hypothetical HIV vaccine trial and more likely to be retained than younger men. In most OECD studies, sex/gender was not associated with WTP in a hypothetical HIV vaccine trial, while females were more likely to be retained in most studies. Largely, age was not associated with WTP in the non-OECD countries, but the results on sex/gender were more variable. The relationship between adolescent or adult WTP in hypothetical HIV vaccine trials in South Africa did not appear to be modified by high school student status. In addition, more studies in discordant couples in the context of HIV vaccine preparedness could be conducted to examine gender roles and inequalities in preparation for HIV vaccine trials.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
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 head0.144
GPT teacher head0.454
Teacher spread0.310 · 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 designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePsychology Health & MedicineSame topicVaccine Coverage and HesitancyFrench-language works237,207