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Record W2113138763 · doi:10.1136/sextrans-2013-051491

Associations between rushed condom application and condom use errors and problems: Table 1

2014· article· en· W2113138763 on OpenAlexaff
Richard Crosby, Cynthia A. Graham, Robin R. Milhausen, Stephanie A. Sanders, William L. Yarber, Lydia A. Shrier

Bibliographic record

VenueSexually Transmitted Infections · 2014
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsUniversity of Guelph
FundersNational Institute of Allergy and Infectious Diseases
KeywordsCondomMedicinePopulationOddsOdds ratioDemographyFamily medicineHuman immunodeficiency virus (HIV)Environmental healthLogistic regressionInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether any of four condom use errors/problems occurred more frequently when condom application was 'rushed' among a clinic-based sample from three US states. METHODS: A convenience sample (n=512) completed daily electronic assessments including questions about condom use being rushed and also assessed condom breakage, slippage, leakage and incomplete use. RESULTS: Of 8856 events, 6.5% (n=574) occurred when application was rushed. When events involved rushed condom application, the estimated odds of breakage and slippage were almost doubled (estimated OR (EOR)=1.90 and EOR=1.86). Rushed application increased the odds of not using condoms throughout sex (EOR=1.33) and nearly tripled the odds of leakage (EOR=2.96). With one exception, all tests for interactions between gender and rushed application and between age and rushed application were not significant (p values>0.10). CONCLUSIONS: This event-level analysis suggests that women and men who perceive that condom application was rushed are more likely to experience errors/problems during the sexual event that substantially compromise the protective value of condoms against disease and pregnancy. Educational efforts emphasising the need to allow ample time for condom application may benefit this population.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.059
GPT teacher head0.366
Teacher spread0.308 · 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.

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

Citations7
Published2014
Admission routes1
Has abstractyes

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