Mapping the integration of social and ethical issues in health technology assessment
Bibliographic record
Abstract
BACKGROUND: Since its inception, the field of health technology assessment (HTA) has stressed the need for consideration of ethical and social issues. However, few concepts or analytic tools have been developed, and because of the complexity of the endeavor and a lack of integration of work already produced, such concepts remain difficult to apply in HTA. OBJECTIVES: Through a descriptive "map" of concepts, tools, and processes, we summarize the most tangible efforts on the part of HTA producers to address social and ethical issues. METHODS: A literature review and content analysis of HTA reports in the Centre for Reviews and Dissemination database enables a synthesis of the reflections on, initiatives around, and gaps in knowledge related to the integration of social and ethical issues in HTA. RESULTS: We examine: (i) the aim of integrating ethical and social issues in HTA, (ii) the theoretical approaches used, (iii) the methods and processes applied, and (iv) the implications for HTA producers. We highlight two levels at which social and ethical issues can be considered: throughout the production process of HTA reports and as part of the organizational structure of HTA agencies. CONCLUSIONS: Given the profound societal changes that occur in relation to healthcare technology development, HTA producers have a responsibility to inform and enlighten technology-related public and policy debates. Fulfilling this role, though, requires that socioethical dimensions of technology and HTA are made explicit.
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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 | 0.350 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.014 | 0.066 |
| Scholarly communication | 0.041 | 0.036 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".