Improving ethics analysis in health technology assessment
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to critically examine the current guidance for conducting ethics analysis in health technology assessment (HTA) and to offer recommendations for how to improve this practice. METHODS: MEDLINE, Philosopher's Index, and Google Scholar were searched for articles and reports using the keywords "ethics" and "health technology assessment" and related terms. Bibliographies of all relevant articles were also examined for additional references. A philosophical analysis of the existing guidance was conducted. RESULTS: We offer three recommendations for improving ethics analysis in HTA. First, ethical and legal issues must be clearly separated so that all policy-relevant questions that the technology raises can be considered clearly and systematically. Second, analysts must make better use of ethics theory and discuss better how particular theoretical approaches and associated analytic tools are selected to make transparent which alternative approaches were considered and why they were rejected. Third, the necessity for philosophical expertise to adequately conduct ethics analysis needs to be acknowledged. CONCLUSIONS: To act on these recommendations for ethics analysis, we offer these three steps forward: acknowledge and use relevant expertise, further develop models for conducting and reporting ethics analyses, and make use of untapped resources in the literature.
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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 | 0.780 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.038 | 0.054 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".