Training Practitioners in Evidence-Based Chronic Disease Prevention for Global Health
Bibliographic record
Abstract
Too often, public health decisions are based on short-term demands rather than long-term research and objectives. Policies and programmes are sometimes developed around anecdotal evidence. The Evidence-Based Public Health (EBPH) programme trains public health practitioners to use a comprehensive, scientific approach when developing and evaluating chronic disease programmes. Begun in 2002, the EBPH programme is an international collaboration. The course is organized in seven parts to teach skills in: 1) assessing a community's needs; 2) quantifying the issue; 3) developing a concise statement of the issue; 4) determining what is known about the issue by reviewing the scientific literature; 5) developing and prioritizing programme and policy options; 6) developing an action plan and implementing interventions; and 7) evaluating the programme or policy. The course takes an applied approach and emphasizes information that is readily available to busy practitioners, relying on experiential learning and includes lectures, practice exercises, and case studies. It focuses n using evidence-based tools and encourages participants to add to the evidence base in areas where intervention knowledge is sparse. Through this training programme, we educated practitioners from 38 countries in 4 continents. This article describes the evolution of the parent course and describes experiences implementing the course in the Russian Federation, Lithuania, and Chile. Lessons learned from replication of the course include the need to build a "critical mass" of public health officials trained in EBPH within each country and the importance of international, collaborative networks. Scientific and technologic advances provide unprecedented opportunities for public health professionals to enhance the practice of EBPH. To take full advantage of new technology and tools and to combat new health challenges, public health practitioners must continually improve their skills.
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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 | 0.045 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".