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Record W2951032344 · doi:10.22374/ijmsch.v2i1.18

Jabs for the Boys

2019· article· en· W2951032344 on OpenAlexvenueno aff
Peter Baker, Gillian Prue, Jamie Rae, David Winterflood, Giampiero Favato

Bibliographic record

VenueInternational Journal of Men s Social and Community Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsVaccinationGovernment (linguistics)Political scienceEquity (law)Human papillomavirusPoliticsPublic healthFamily medicineMedicinePublic relationsPublic administrationLawImmunologyNursing

Abstract

fetched live from OpenAlex

The human papillomavirus (HPV) can cause a range of cancers as well as genital warts and recurrent respiratory papillomatosis in men and women. Most cases can be prevented by vaccination in adolescence. Many countries vaccinate girls and an increasing number, although still a minority, vaccinate both boys and girls. The case for vaccinating boys is based on arguments of public health, equity, ethics, and cost-effectiveness. The selective vaccination of females does not protect males sufficiently and provides no protection at all for men who have sex with men. In the United Kingdom (UK), the government’s vaccination advisory committee (Joint Committee on Vaccination and Immunisation [JCVI]) began to consider whether boys should be vaccinated as well as girls in 2013 and made clear in draft statements that it considered this not to be cost-effective. A campaign group, HPV Action, was established to advocate gender-neutral vaccination. This group became a coalition of over 50 organisations and used evidence-based arguments, political advocacy and media campaigning to make its case. One of its members initiated legal action against the government on the grounds of sex discrimination. In July 2018, the government agreed that boys in the UK should be vaccinated. The lessons for other campaigns in the men’s and public health fields include: be prepared for the long haul, focus on clear and specific goals, build alliances, align the objectives with existing policies, make a financial case for a change of policy, and use all legitimate means to exert pressure.

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.451
Threshold uncertainty score0.783

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0060.004
Open science0.0020.006
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.4510.262

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 head0.063
GPT teacher head0.406
Teacher spread0.343 · 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.

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
Published2019
Admission routes1
Has abstractyes

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