Methodological guidance documents for evaluation of ethical considerations in health technology assessment: a systematic review
Bibliographic record
Abstract
Despite the advances made in the development of ethical frameworks for health technology assessment (HTA), there is no clear agreement on the scope and details of a practical approach to address ethical aspects in HTA. This systematic review aimed to identify existing guidance documents for incorporation of ethics in HTA to provide an overview of their methodological features. The review identified 43 conceptual frameworks or practical guidelines, varying in their philosophical approach, structure, and comprehensiveness. They were designed for different purposes throughout the HTA process, ranging from helping HTA-producers in identification, appraisal and analysis of ethical data to supporting decision-makers in making value-sensitive decisions. They frequently promoted using analytical methods that combined normative reflection with participatory approaches. The choice of a method for collection and analysis of ethical data seems to depend on the context in which technology is being assessed, the purpose of analysis, and availability of required resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.456 | 0.669 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.039 | 0.031 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".