Whose Burden? Synthesizing Evidence from Diverse Perspectives for a Comprehensive Description of Disease Burden.
Bibliographic record
Abstract
INTRODUCTION: To address public concerns about a specific health threat and develop effective public health strategies aimed at reducing related health risks, it is necessary to describe the extent of the health threat in the target population. This typically involves assessing the impact of the health threat using quantitative measurement of pertinent epidemiologic and economic indicators. While existing literature espouses the benefits of building collective knowledge to capture the depth and complexity of health and disease, there is limited information about the most effective ways to synthesize different forms of evidence to construct a comprehensive assessment of the burden of disease. METHODS: Research is currently underway in northern Canadian Aboriginal communities concerned over their high prevalence of Helicobacter pylori infection and the associated risk of stomach cancer. This community-driven research program will be used to illustrate the value of incorporating multiple perspectives in characterizations of disease burden when attempting to address public health concerns, with emphasis on the application of methods for synthesizing diverse types of evidence on disease burden.
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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 | 0.254 | 0.466 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.038 | 0.026 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".