Clinical epidemiology
Bibliographic record
Abstract
Abstract Global society has reached a level of interdependence wherein there is a need to share healthcare knowledge and deploy resources in the best interests of people everywhere. Clinical and public health professionals can be united in this effort through their common reliance on epidemiology. Clinical epidemiology and its derivative—the evidence-based medicine movement—have many parallels with public health. Indeed, many clinicians with clinical epidemiology training develop research projects and subsequently research programmes that move beyond clinical decision-making to include a population focus. In response to this global need, the International Clinical Epidemiology Network (INCLEN) programme has trained over 700 physicians and other health specialists at a Master’s degree level in clinical epidemiology, social sciences, biostatistics, or clinical economics. INCLEN has established a global resource network to support fundamental changes in the way physicians, medical educators, and policy makers think about health and disease. INCLEN now has semi-autonomous regional networks in Africa, India, China, Southeast Asia, Latin America, Europe–Mediterranean, and Canada–United States. A methods framework, the ‘equity–effectiveness iterative loop’, is used to demonstrate the interface between clinical epidemiology and public health, with special attention to ensuring that the disadvantaged are explicitly considered. The focus is on evidence-based, action-oriented epidemiology based upon the health needs of the relevant community. Various examples are used, such as circumcision to prevent male-acquired HIV infection.
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 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.197 | 0.069 |
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".