The Contribution of Health Technology Assessment, Health Needs Assessment, and Health Impact Assessment to the Assessment and Translation of Technologies in the Field of Public Health Genomics
Bibliographic record
Abstract
The European Union has named genomics as one of the promising research fields for the development of new health technologies. Major concerns with regard to these fields are, on the one hand, the rather slow and limited translation of new knowledge and, on the other hand, missing insights into the impact on public health and health care practice of those technologies that are actually introduced. This paper aims to give an overview of the major assessment instruments in public health [health technology assessment (HTA), health needs assessment (HNA) and health impact assessment (HIA)] which could contribute to the systematic translation and assessment of genomic health applications by focussing at population level and on public health policy making. It is shown to what extent HTA, HNA and HIA contribute to translational research by using the continuum of translational research (T1-T4) in genomic medicine as an analytic framework. The selected assessment methodologies predominantly cover 2 to 4 phases within the T1-T4 system. HTA delivers the most complete set of methodologies when assessing health applications. HNA can be used to prioritize areas where genomic health applications are needed or to identify infrastructural needs. HIA delivers information on the impact of technologies in a wider scope and promotes informed decision making. HTA, HNA and HIA provide a partly overlapping and partly unique set of methodologies and infrastructure for the translation and assessment of genomic health applications. They are broad in scope and go beyond the continuum of T1-T4 translational research regarding policy translation.
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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 | 0.178 | 0.193 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".