ETHICS IN HEALTH TECHNOLOGY ASSESSMENT: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
OBJECTIVES: Integration of ethics into health technology assessment (HTA) remains challenging for HTA practitioners. We conducted a systematic review on social and methodological issues related to ethical analysis in HTA. We examined: (1) reasons for integrating ethics (social needs); (2) obstacles to ethical integration; (3) concepts and processes deployed in ethical evaluation (more specifically value judgments) and critical analyses of formal experimentations of ethical evaluation in HTA. METHODS: Search criteria included "ethic," "technology assessment," and "HTA". The literature search was done in Medline/Ovid, SCOPUS, CINAHL, PsycINFO, and the international HTA Database. Screening of citations, full-text screening, and data extraction were performed by two subgroups of two independent reviewers. Data extracted from articles were grouped into categories using a general inductive method. RESULTS: A list of 1,646 citations remained after the removal of duplicates. Of these, 132 were fully reviewed, yielding 67 eligible articles for analysis. The social need most often reported was to inform policy decision making. The absence of shared standard models for ethical analysis was the obstacle to integration most often mentioned. Fairness and Equity and values embedded in Principlism were the values most often mentioned in relation to ethical evaluation. CONCLUSIONS: Compared with the scientific experimental paradigm, there are no settled proceedings for ethics in HTA nor consensus on the role of ethical theory and ethical expertise hindering its integration. Our findings enable us to hypothesize that there exists interdependence between the three issues studied in this work and that value judgments could be their linking concept.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.009 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".