The VALIDATE handbook. An approach on the integration of values in doing assessments of health technologies
Bibliographic record
Abstract
Health Technology Assessment (HTA) is defined as a multidisciplinary process that uses explicit methods to determine the value of a health technology at different points in its lifecycle. The purpose is to inform decision-making in order to promote an equitable, efficient, and high-quality health system. The definition reflects that facts and values are intertwined in HTA. This means that HTA should be considered as a type of policy analysis, wherein the assessment of safety, clinical and cost implications of health technologies, as well as their wider ethical, legal, social, organizational, environmental and other implications is conducted from the view that these aspects are closely interrelated, and wherein stakeholders are involved in a more productive way throughout the process of HTA. Acknowledging this holds the potential of conducting assessments of health technologies in a way that supports deliberative democratic decision making. In the 2018-2021 EU Erasmus+ strategic partnerships project “VALues In Doing Assessments ofhealthcare TEchnologies” (VALIDATE), a consortium of seven academic and HTA organizations have developed an approach to HTA that allows for the integration of empirical analysis and normative inquiry. The VALIDATE handbook: an approach on the integration of values in doing assessments of health technologies offers the reader an opportunity to get acquainted with the theoretical considerations and apprehend the associated practical and organizational implications of this approach. It offers those interested in HTA to integrate empirical analysis and normative inquiry in a transparent way.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".