Phase specific approaches to the epidemiology and prevention of sexually transmitted diseases
Bibliographic record
Abstract
An overview The recent past has brought an increased appreciation of the temporal dimension in sexually transmitted disease (STD) epidemiology and prevention science. At the individual level there is greater focus on the temporal ordering of people's sexual partnerships. Concepts like sexual trajectories, concurrency, and the gap between partnerships now attract attention as risk factors for the acquisition and transmission of STDs.1–3 At the population level, the evolution of STD epidemics by predictable phases, characterised by changing patterns in the distribution and transmission of STD pathogens within and between subpopulations, has been a focus of recent work.4–7 Temporal concepts in STD epidemiology have also been markedly enriched through the impact of mathematical modelling.8 There are important links between the temporal dimensions of individual behaviours and epidemic dynamics. The prevalence of particular sexual behaviour trajectory types, concurrent partnerships, and short gaps between partnerships within a population are increasingly considered important determinants of population prevalence and incidence of STDs, and of their rate of spread. Finally, the recent movement in epidemiology in general, towards a focus on social determinants of health conditions and on the historical evolution of those social determinants is also observable in the STD field, and contributes to increased appreciation of evolutionary frameworks.9 Temporal changes at both the individual and population levels will be influenced greatly by alterations in the social, demographic, cultural, and political context. For example, changes in societal parameters such as the political economy and the sociolegal system are important influences on individual patterns of sexual partnership formation and dissolution, and will also affect the nature of sexual networks within the population. In addition, these societal factors will influence other determinants of STD spread such as the availability, accessibility, and utilisation of appropriate health care, and availability and utilisation of …
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".