Evaluating facts and facting evaluations: On the fact‐value relationship in<scp>HTA</scp>
Bibliographic record
Abstract
Health technology assessment (HTA) is an evaluation of health technologies in terms of facts and evidence. However, the relationship between facts and values is still not clear in HTA. This is problematic in an era of "fake facts" and "truth production." Accordingly, the objective of this study is to clarify the relationship between facts and values in HTA. We start with the perspectives of the traditional positivist account of "evaluating facts" and the social-constructivist account of "facting values." Our analysis reveals diverse relationships between facts and a spectrum of values, ranging from basic human values, to the values of health professionals, and values of and in HTA, as well as for decision making. We argue for sensitivity to the relationship between facts and values on all levels of HTA, for being open and transparent about the values guiding the production of facts, and for a primacy for the values close to the principal goals of health care, ie, relieving suffering. We maintain that philosophy (in particular ethics) may have an important role in addressing the relationship between facts and values in HTA. Philosophy may help us to avoid fallacies of inferring values from facts; to disentangle the normative assumptions in the production or presentation of facts and to tease out implicit value judgements in HTA; to analyse evaluative argumentation relating to facts about technologies; to address conceptual issues of normative importance; and to promote reflection on HTA's own value system. In this we argue for a(n Aristotelian) middle way between the traditional positivist account of "evaluating facts" and the social-constructivist account of "facting values," which we call "factuation." We conclude that HTA is unique in bringing together facts and values and that being conscious and explicit about this "factuation" is key to making HTA valuable to both individual decision makers and society as a whole.
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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 | 0.061 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.017 | 0.034 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".