QUALITY ASSESSMENT OF ETHICS ANALYSES FOR HEALTH TECHNOLOGY ASSSESSMENT
Bibliographic record
Abstract
OBJECTIVES: Although consideration of ethical issues is recognized as a crucial part of health technology assessment, ethics analysis for HTA is generally perceived as methodologically underdeveloped in comparison to other HTA domains. The aim of our study is (i) to verify existing tools for quality assessment of ethics analyses for HTA, (ii) to consider some arguments for and against the need for quality assessment tools for ethics analyses for HTA, and (iii) to propose a preliminary set of criteria that could be used for assessing the quality of ethics analyses for HTA. METHODS: We systematically reviewed the literature, reviewed HTA organizations' Web sites, and solicited views from thirty-two experts in the field of ethics for HTA. RESULTS: The database and HTA agency Web site searches yielded 420 references (413 from databases, seven from HTA Web sites). No formal instruments for assessing the quality of ethics analyses for HTA purposes were identified. Thirty-two experts in the field of ethics for HTA from ten countries, who were brought together at two workshops held in Edmonton (Canada) and Cologne (Germany) confirmed the findings from the literature. CONCLUSIONS: Generating a quality assessment tool for ethics analyses in HTA would confer considerable benefits, including methodological alignment with other areas of HTA, increase in transparency and transferability of ethics analyses, and provision of common language between the various participants in the HTA process. We propose key characteristics of quality assessment tools for this purpose, which can be applied to ethics analyses for HTA purposes.
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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 | 0.724 | 0.878 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.041 | 0.038 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".