Abstracts presented at the 7th World Alliance for Risk Factor Surveillance (WARFS) Global Conference. October 16-19, 2011. Toronto, Ontario, Canada.
Bibliographic record
Abstract
The 7th World Alliance for Risk Factor Surveillance (WARFS) Global Conference, hosted by the Public Health Agency of Canada, was held in Toronto, Ontario, Canada, from October 16 to 19, 2011. Previous WARFS conferences were held in USA (1999), Finland (2001), Australia (2003), Uruguay (2005) and Italy (2007, 2009). WARFS is a global working group on surveillance under the International Union for Health Promotion and Education (IUHPE) It supports the development of risk factor surveillance as a tool for evidence-based public health, acknowledging the importance of this source of information to inform, monitor and evaluate disease prevention and health promotion policies and programs. The theme of the 2011 Global Conference was the role of surveillance in the promotion of health. The Global Conference had 146 registered participants, making it the second most attended WARFS conference in its history. Over the three days, participants attended oral and poster presentations from 30 countries. The conference would not have been possible without the hard work of the International Scientific Committee and the Local Organizing Committee. To highlight the importance and the significance of this conference at an international level, Chronic Diseases and Injuries in Canada (CDIC) is pleased to publish this supplementary issue, which contains 70 abstracts presented at the 7th WARFS Global Conference. In the spirit the Global Conference, this collection of abstracts brings together surveillance material on risk factors, chronic diseases, infectious diseases and injuries from around the world. By making these abstracts widely available, CDIC hopes to further the conference objectives through a continued dialogue between those interested in linking risk factor surveillance to health promotion.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.462 | 0.189 |
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".