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Record W1978668755 · doi:10.1136/jech.2008.084509

Good girls do…get vaccinated: HPV, mass marketing and moral dilemmas for sexually active young women

2009· article· en· W1978668755 on OpenAlexaffabout
Jessica Polzer, Susan Knabe

Bibliographic record

VenueJournal of Epidemiology & Community Health · 2009
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineSexually activeMass mediaFamily medicineAdvertisingHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

Current media representations of human papillomavirus (HPV) vaccination communicate a potent message that concerns about sexually transmitted infection (STI) are no longer restricted to sexually active “bad girls”. The mass marketing of vaccines against HPV – whether to serve industry efforts to establish markets for products or public health efforts to minimise STIs and cancer morbidity and mortality – firmly yet carefully inscribes the not-yet-sexually-active “good girl” as the primary target in the control of sexually transmitted disease. The rationale for HPV vaccination of girls prior to sexual contact in Canada and elsewhere derives from the high prevalence of the virus (which is typically described as the most common STI worldwide), its ease of transmission through non-penetrative, skin-to-skin contact and the relatively high incidence of HPV infection after onset of sexual relations.1 Because all forms of sexual contact are considered likely to expose girls/teens to HPV, the delivery and mass marketing of HPV vaccination prior to sexual activity frame parental decisions to vaccinate as the right, reasonable and responsible choice to “protect” their daughters and arm them “for life” in the ongoing war against cancer.2 3 Unlike the girls targeted by the school-based vaccination programmes, young women who fall outside the recommended age range for vaccination, and those who are already sexually active, are left in a kind of limbo that stems from the disconnect between mass marketing messages and clinical evidence. On the one hand, the …

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)0.008
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.015
Scholarly communication0.0080.008
Open science0.0010.003
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0090.001

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 head0.114
GPT teacher head0.443
Teacher spread0.329 · 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 designQualitative
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

Citations21
Published2009
Admission routes2
Has abstractyes

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