Eliciting Value-Judgments in Health Technology Assessment: An Applied Ethics Decision Making Paradigm
Bibliographic record
Abstract
The worldwide COVID-19 pandemic has shed more light on the difficulty of making health care decisions integrating scientific knowledge and values associated to life and death issues, human suffering, quality of life, economic losses, liberty of movement, etc. But the difficulties related to health care decisions and the use of innovative drugs or technologies are not new, and many countries have created agencies that have the mandate to evaluate new technologies in health care. Health Technological Assessment (HTA) reports’ aim is to guide the decision makers in these difficult matters. There are two ethical components in HTA. The first is the report’s presentation of an ethical evaluation of the technology. The second is the value-ladenness of the HTA decision-making process itself. When implicit value judgments are not elicited, the justification of the final decision cannot be transparent. The present paper aims to identify and elicit the implicit value-judgments related to each step of the HTA process. This research is grounded on an applied ethics decision-making paradigm based on the role of value judgments in the decision-making process. The first part discusses two different approaches to values and value judgments in HTA. In the second part, citations mentioning value judgments extracted from a systematic review on the integration of ethics into HTA were categorized to elicit the value judgments and their criteria for each different HTA decision-making steps. The results show that there are 18 decision-making steps in the HTA process where 23 implicit value-judgments can be recognized. The range of these value judgments encompasses the whole HTA process: from the initial request, the presenting of the principal issues, to the final report’s dissemination. Since stakeholders need to understand which value judgments the conclusion of a report relies on, eliciting the implicit value judgments in the HTA decision-making process should yield more transparency.
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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 | 0.288 | 0.356 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.009 | 0.012 |
| 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".