Implementation of a Surveillance and Monitoring System for Chronic Disease: A Scoping Review
Bibliographic record
Abstract
Background: Numerous national-based indicators aid in designing population-centered chronic disease surveillance. However, there is little consensus on the key indicators and goals for developing a chronic disease surveillance analysis system. Objective: This study aimed to develop evidence-based indicators to measure and improve the health outcomes of chronic disease surveillance systems in Korea. Methods: A scoping review was focused on peer-reviewed literature from 2012 to 2022 using PubMed, Medline, Embase, PsychINFO, CINHAL, Wiley Online, Scopus, and Cochrane Library. Three reviewers evaluated and selected the articles in accordance with comprehensive inclusion and exclusion criteria. Data were synthesized using median descriptive analysis, prioritization, and agreement. There was a consensus meeting to discuss the recommendations, and the findings were analyzed thematically and descriptively. Results: Forty-eight articles were finalized and most of them were published in 2019 (18.8%). The studies on chronic disease-based surveillance systems and related data sources were conducted primarily in Canada (58.3%). The findings were prioritized by prevalence rate, risk factors, data linkage, complex disease, and updated indicators in a current system. The key themes of focus were mortality, survival management, diagnosis, treatment, and healthcare systems. Conclusion : Our preliminary measurement methods need validation through follow-up projects. Chronic disease surveillance will improve public health resource allocation and monitoring in real time. Public health and healthcare systems could be enhanced through timely assessments of population health at the local and regional levels. In Korea, experts will validate the chronic disease-based surveillance system’s development.
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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 | 0.077 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".