Overview: the role of emergent properties of complex systems in the epidemiology and prevention of sexually transmitted infections including HIV infection
Bibliographic record
Abstract
This supplement of Sexually Transmitted Infections highlights the role of emergent properties of complex systems in the epidemiology and prevention of sexually transmitted infections (STI) including HIV infection. All units of observation, measurement and intervention in STI epidemiology and prevention, including populations, behaviours, intervention packages and health systems through which interventions are delivered constitute complex systems with emergent properties. Emergent properties of complex systems thus play an important role in the extent and patterns of spread of infection; they also need to be considered in the development of prevention strategies including the choice of intervention packages; target populations and required coverage and duration of interventions. The following overview describes types of emergent properties of complex systems, many of which are reflected in the 15 articles compiled in the supplement, and summarises the main issues presented by the authors from theoretical, methodological, practical and policy perspectives. The STI epidemiology and prevention literature has long reflected the recognition of multiple levels of causation of epidemiological patterns, prevention strategies and their impact.1 2 Discussions of individual and population-level approaches to epidemiology and prevention,1 and multilevel strategies for analysis of epidemiological data and planning and implementation of preventive interventions,2 have been around for over a decade and are well accepted. However, despite their inclusion in earlier publications, the constructs of complex systems marked by emergent properties inherent in the concepts of ‘population’ and ‘multilevel’, which have received increased attention in epidemiology in recent years, have not made their way into mainstream epidemiological and prevention thought in the STI/HIV field. Simply stated, a complex system is made up of a large number 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".