The role of surveillance and data use in the development of public health policies
Bibliographic record
Abstract
Decision makers consider numerous factors besides surveillance data in establishing public health policies and programmes. In an evidence-informed system, it is important to collect, interpret, and present information that has maximum impact on the broader policy agenda.Successful policies and programmes are rational, feasible, and practical, with wide public support. Surveillance systems must align and interact with the other parts of the policy infrastructure. There must be continuous links between data providers, collectors, and users. Data must be representative of population variations.For chronic diseases, the major challenge is multiple risks. Surveillance systems must capture many factors from many sources. Data must be presented in plain language and tailored to the needs of various users - politicians, policy makers, health providers, researchers, and the public. Data must be linked to other policy areas such as taxation. Economic arguments, including modelling, strongly influence decisions. Broad data ownership through alliances also has significant impact.
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 | 0.421 | 0.491 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.027 | 0.048 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".