Organizational structure and funding of health technology assessment agencies around the world
Bibliographic record
Abstract
Aim : analyze the structure and funding of health technology assessment (HTA) agencies abroad. Materials and methods . Here, we review the organizational structure and funding of HTA agencies in Europe (Austria, Belgium, Germany, Ireland, the Netherlands, the United Kingdom, France, and Sweden), Canada and Australia. The relevant information was found on web-sites of HTA agencies, in the Medline database, and via the searching engines Yandex and Google; the search was conducted using the specific descriptors: «organizational structure of HTA agency», «funding of HTA agency», «pharmaceutical», «reimbursement», «healthcare decision-making», and «funding». Results. The identified HTA-agencies may have a status of either government-funded or nonprofit organization or a structural element of a governmental body. These hTa agencies are funded mainly from the national budget. The funding varies from €550 000 for Ireland to £63.1 mln (€70 million) for the National Institute for Clinical Excellence (NICE) in the UK. The number of employees in the reviewed HTA agencies varies from 6.8 full time employees (FTE) in the Health Information and Quality Authority (HIQA) in Ireland to 604 FTEs in the NICE.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".