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Record W2154114279 · doi:10.1177/1524839903257307

Lesbian Health Matters: A Pap Test Education Campaign Nearly Thwarted by Discrimination

2004· article· en· W2154114279 on OpenAlexaff
Ellen Phillips‐Angeles, Paula Wolfe, Robin Myers, Patricia Dawson, Jeanne Marrazzo, Sallye Soltner, Mary Dzieweczynski

Bibliographic record

VenueHealth Promotion Practice · 2004
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsLesbianNewspaperPublicityPap testTest (biology)MedicinePublic healthCervical cancerHealth educationFamily medicineCervical cancer screeningPsychologyAdvertisingNursingCancerPolitical scienceBusiness

Abstract

fetched live from OpenAlex

The Pap test detects cell changes in the cervix that can be treated, preventing cancer from developing. Regular screening reduced cervical cancer deaths by 70% since 1950. Lesbians may not be adequately screened because of a misperception that they do not need Pap tests. The "Lesbian Health Matters" public and provider education campaign was implemented to address this problem. Paid advertisements were placed on two radio stations and in four newspapers. After 1 week, both radio stations cancelled the ads due to listener complaints about hearing the word "lesbian" on the radio. The community responded to this discriminatory action by demanding the campaign be completed, creating publicity that increased the campaign's reach to 34% of women in the region. A training program was implemented reaching 219 providers. Thirty-two hundred health providers were surveyed regarding lesbian-friendly practice. A database of 293 providers was created and 120 referrals made.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.056
GPT teacher head0.450
Teacher spread0.394 · 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

Citations18
Published2004
Admission routes1
Has abstractyes

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