Tackling ethical issues in health technology assessment: A proposed framework
Bibliographic record
Abstract
OBJECTIVES: Values are intrinsic to the use of health technology assessments (HTAs) in health policy, but neglecting value assumptions in HTA makes their results appear more robust or normatively neutral than may be the case. Results of a 2003 survey by the International Network of Agencies for Health Technology Assessment (INAHTA) revealed the existence of disparate methods for making values and ethical issues explicit when conducting HTA. METHODS: An Ethics Working Group, with representation from sixteen agencies, was established to develop a framework for addressing ethical issues in HTA. Using an iterative approach, with email exchanges and face-to-face workshops, a report on Handling Ethical Issues was produced. RESULTS: This study describes the development process and the agreed upon framework for reflexive ethical analysis that aims to uncover and explore the ethical implications of technologies through an integrated, context-sensitive approach and situates the proposed framework within previous work in the development of ethics analysis in HTA. CONCLUSIONS: It is important that methodological approaches to address ethical reflection in HTA be integrative and context sensitive. The question-based approach described and recommended here is meant to elicit this type of reflection in a way that can be used by HTA agencies. The questions proposed are considered only as a starting point for handling ethics issues, but their use would represent a significant improvement over much of the existing practice.
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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 | 0.163 | 0.082 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.014 | 0.074 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".