Ethical Evaluation in Health Technology Assessment: A Challenge for Applied Philosophy
Bibliographic record
Abstract
The integration of ethical analysis in Health Technology Assessment (HTA) has proven difficult to implement even though it is explicitly recognized as an important component of such assessments in HTA literature. When compared to the standardized scientific method for systematic reviews in HTA, the diversity of ethical analysis has been characterized as a fundamental barrier to the integration of ethics. The present paper aims to identify the theoretical and practical differences between the approaches underpinning ethical analysis in HTA and clarify the reasons for such diversity. Our systematic review of HTA literature pertaining to the barriers to the integration of ethics in HTA identified nine ethical approaches: Principlism, Casuistry, Coherence Analysis, Wide Reflective Equilibrium, Axiology, the Socratic approach, the Triangular model, Constructive Technology Assessment and Social Shaping of Technology. Citations pertaining to each approach were extracted and categorized according to three constitutive components of ethical argumentation established in a previous research evaluating nanotechnologies: i) the disciplinary foundation that grounds the validity of the ethical evaluation, ii) the characteristics of such evaluation, iii) the operational process involved in applying it to a particular case (i.e., its practical reasoning). This comparison shows that, 1) the difference between these approaches rests primarily on their disciplinary foundation (rooted in philosophy, philosophy/theology, or sociology), 2) their complexity can be observed in the distinct characteristics of ethical evaluation deriving from their differing disciplinary foundation, and 3) although four different types of operationalization procedure were identified, little information was available in regards to the practical reasoning associated with these approaches.
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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 | 0.540 | 0.392 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.011 | 0.156 |
| Scholarly communication | 0.036 | 0.044 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.019 | 0.043 |
| Insufficient payload (model declined to judge) | 0.002 | 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".