Mapping capacity to conduct health technology assessment in Central, Eastern and South-Eastern Europe
Bibliographic record
Abstract
AIM: To provide insights into the capacity to conduct health technology assessment (HTA) in Central, Eastern, and South-Eastern Europe (CESEE), taking account of technical, financial, networking, and human resources. METHODS: An e-mail survey of 257 CESEE key informants involved in HTA was undertaken between March and April 2014. Contact e-mail addresses were identified from the internet. The survey questionnaire consisted of 3 sections: i) characteristics of the organization performing HTA, (ii) networking in HTA, and (iii) resources allocated for HTA. RESULTS: The survey was completed by 41 respondents representing a wide range of institutions from CESEE countries (response rate of 19.8%). Less than a quarter of respondents reported that their institutions had HTA-specific budgets, whereas the majority indicated that their institutions participated in HTA networks either at domestic or international levels. Although almost half of respondents indicated that their institutions offered HTA training, a shortage in skills training was suggested as the main barrier to HTA. CONCLUSION: This is the first survey to thoroughly assess the state of HTA capacity in the CESEE region. To strengthen HTA capacity, CESEE countries should increase financial, technical, and training resources. To strengthen collaboration, the European Union and other international bodies should assist existing HTA networks in fulfilling their regional activities through leadership, advocacy to local policymakers, funding, and technical assistance.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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".