Sexual Webs Model for the Examination of Unsafe Sexual Behaviors and the Spread of Sexually Transmitted Diseases Including HIV/AIDS
Bibliographic record
Abstract
Unsafe sex is the second most important risk factor for disability and deaths in the poorest countries and the ninth most important in developed countries. Globally, 30.8 million adults are living with HIV/AIDS and 340 million people are infected annually with sexually transmitted diseases. Unwanted pregnancies and sexually transmitted diseases including HIV/AIDS had been inexorably linked to sex, yet, there is no health behavior model focusing squarely on sexual attributes to provide analytical framework for the examination of unsafe sexual behaviors and the spread of sexual transmitted diseases including HIV/AIDS. This hinders the understanding of the roles of sexual attributes and contextual factors in influencing unsafe sex and the spread of related infections. The ‘Sexual Webs model’ has been constructed based on the individuals’ sexual attributes; levels of entanglement into the “sexual networks” known as “Sexual webs” for the examination of contextual issues influencing unsafe sexual behavior and the spread of sexually transmitted diseases including HIV/AIDS. Published qualitative research articles on sexual behaviors, and health behavior models were selected from the internet using Google and Google Scholar search. The research findings were synthesized using meta-ethnographic analysis. Research endeavors using the postulates of this model would provide better insight on the contextual issues influencing unsafe sexual behavior for policy formulation and program interventions to promote safe sexual practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".