We need to talk about values: a proposed framework for the articulation of normative reasoning in health technology assessment
Bibliographic record
Abstract
It is acknowledged that health technology assessment (HTA) is an inherently value-based activity that makes use of normative reasoning alongside empirical evidence. But the language used to conceptualise and articulate HTA's normative aspects is demonstrably unnuanced, imprecise, and inconsistently employed, undermining transparency and preventing proper scrutiny of the rationales on which decisions are based. This paper - developed through a cross-disciplinary collaboration of 24 researchers with expertise in healthcare priority-setting - seeks to address this problem by offering a clear definition of key terms and distinguishing between the types of normative commitment invoked during HTA, thus providing a novel conceptual framework for the articulation of reasoning. Through application to a hypothetical case, it is illustrated how this framework can operate as a practical tool through which HTA practitioners and policymakers can enhance the transparency and coherence of their decision-making, while enabling others to hold them more easily to account. The framework is offered as a starting point for further discussion amongst those with a desire to enhance the legitimacy and fairness of HTA by facilitating practical public reasoning, in which decisions are made on behalf of the public, in public view, through a chain of reasoning that withstands ethical scrutiny.
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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 | 0.089 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.013 | 0.104 |
| Scholarly communication | 0.028 | 0.037 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".