REVEALING AND ACKNOWLEDGING VALUE JUDGMENTS IN HEALTH TECHNOLOGY ASSESSMENT
Bibliographic record
Abstract
BACKGROUND: Although value issues are increasingly addressed in health technology assessment (HTA) reports, HTA is still seen as a scientific endeavor and sometimes contrasted with value judgments, which are considered arbitrary and unscientific. This article aims at illustrating how numerous value judgments are at play in the HTA process, and why it is important to acknowledge and address value judgments. METHODS: A panel of experts involved in HTA, including ethicists, scrutinized the HTA process with regard to implicit value judgments. It was analyzed whether these value judgments undermine the accountability of HTA results. The final results were obtained after several rounds of deliberation. RESULTS: Value judgments are identified before the assessment when identifying and selecting health technologies to assess, and as part of assessment. They are at play in the processes of deciding on how to select, frame, present, summarize or synthesize information in systematic reviews. Also, in economic analysis, value judgments are ubiquitous. Addressing the ethical, legal, and social issues of a given health technology involves moral, legal, and social value judgments by definition. So do the appraisal and the decision-making process. CONCLUSIONS: HTA by and large is a process of value judgments. However, the preponderance of value judgments does not render HTA biased or flawed. On the contrary they are basic elements of the HTA process. Acknowledging and explicitly addressing value judgments may improve the accountability of HTA.
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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 | 0.684 | 0.779 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.018 | 0.094 |
| Scholarly communication | 0.036 | 0.043 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.024 | 0.030 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".