ArticleBMC public health2026
Findings from a transformer-based prediction model: social norms and confidentiality are associated with STIs risk in British men and women.
Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundWomen were disproportionately affected by sexually transmitted infections (STIs), both physically and psychologically. The underlying reasons are likely multifaceted, ranging from individual behavior to relationship power, gender norms, and economic inequities. This study aims to identify predictors of STIs risk in women and men and to explore gender differences in the behavioral patterns that may contribute to STIs risk.
methodsWe analyzed the data from the third United Kingdom National Survey of Sexual Attitudes and Lifestyles, including 15,162 participants aged 16–74 from 2010 to 2012. According to the five levels of individual, interpersonal, community, institutional, and structural in the socio-ecological model (SEM), we selected 119 features from the dataset. Then we applied three deep learning algorithms and two traditional machine learning algorithms to address the influential factors of STIs. To evaluate the performance, we computed the metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall. The results interpretation of the best model is based on feature importance analysis within the context of SEM.
resultsThe tabular transformer model, FT-transformer, demonstrated excellent performance in predicting STIs risk in British males (AUC = 0.843, Accuracy = 87.0%) and females (AUC = 0.879, Accuracy = 87.5%) among five models. The top 10 influential factors to predict STIs risk for British males and females are different. The most influential factor for males is perceived social norms, and for females is guaranteed confidentiality.
conclusionThe high accuracy of the transformer model in predicting STIs risk highlights the need to use multi-level factors to identify gender-specific risk factors, which could be used in the future to formulate gender-tailored interventions in STIs prevention, diagnosis, and treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.