Driving policy dialogue on health technology assessment in Eastern Europe and Central Asia: reporting from an initiative of Health Technology Assessment International
Bibliographic record
Abstract
Eastern Europe and Central Asia (EECA) represents a diverse region facing complex healthcare challenges, including resource constraints, fragmented systems, and limited access to evidence-based decision-making tools. Health technology assessment (HTA) offers a critical framework for addressing these issues by informing efficient allocation of healthcare resources. In April 2024, HTA International (HTAi) convened a policy dialogue in Astana, Kazakhstan, bringing together stakeholders from 12 EECA countries and international experts to discuss HTA advancement in the region. The dialogue highlighted systemic barriers, including political instability, capacity shortages, and fragmented data sources while exploring successful HTA implementation models in some countries. Participants emphasized the importance of political commitment, institutional frameworks, and capacity building, alongside fostering stakeholder collaboration. International organizations such as HTAi and WHO were recognized as vital enablers for technical support and knowledge sharing. Key outcomes included actionable recommendations: strengthening political advocacy, developing legal and institutional frameworks, investing in workforce development, and enhancing multistakeholder engagement. The dialogue underscored HTAi's role in catalyzing regional collaboration, providing platforms for discussion, and offering resources for capacity building. Future initiatives will focus on addressing structural weaknesses, promoting transparency, and embedding HTA into national healthcare systems to ensure equitable and evidence-based decisions. The findings reinforce the potential of HTA to enhance healthcare policy and planning in EECA, fostering resilient systems that better meet population health needs despite ongoing challenges.
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 | 0.140 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".