Building capacity in horizon scanning, early awareness, and disinvestment: a framework for education and training
Bibliographic record
Abstract
OBJECTIVES: The increasing relevance of horizon scanning (HS), early awareness (EA), and disinvestment (DIS) highlights the need for a structured approach to capacity building. Although these fields are essential for evidence-based policy decisions, a harmonized education and training framework to develop necessary competencies is lacking. This article presents the development of a curriculum designed to address this gap in training. METHODS: A transdisciplinary working group was established, drawing on international stakeholders from academia, the public sector, and industry. Using an iterative consensus-driven approach, the group developed a modular curriculum. The curriculum design incorporated best practices from existing education programs in related fields and emphasized case-based learning strategies to ensure contextual adaptability. RESULTS: The resulting curriculum covers theoretical foundations, practical applications, and decision-making processes related to HS, EA, and DIS in eight modules. It supports diverse learner needs, including trainees, training institutions, and public and private organizations, and is designed to be flexible, scalable, and applicable across different regional and organizational contexts. CONCLUSIONS: This curriculum initiative represents a major step toward harmonizing capacity building in HS, EA, and DIS. It fosters sustainability, enhances global health system preparedness, and provides a structured educational platform to support the effective integration of emerging health technologies and evidence-based disinvestment strategies.
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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 | 0.024 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".