How does HTA addresses current social expectations? An international survey
Bibliographic record
Abstract
OBJECTIVES: Integration of ethics into technology assessment in healthcare (HTA) reports is directly linked to the need of decision makers to provide rational grounds justifying their social choices. In a decision-making paradigm, facts and values are intertwined and the social role of HTA reports is to provide relevant information to decision makers. Since 2003, numerous surveys and discussions have addressed different aspects of the integration of ethics into HTA. This study aims to clarify how HTA professionals consider the integration of ethics into HTA, so an international survey was conducted in 2018 and the results are reported here. METHODS: A survey comprising twenty-two questions was designed and carried out from April 2018 to July 2018. Three hundred and twenty-eight HTA agencies from seventy-five countries were invited to participate in this survey. RESULTS: Eighty-nine participants completed the survey, representing a participation rate of twenty-seven percent. As to how HTA reports should fulfill their social role, over 84 percent of respondents agreed upon the necessity to address this role for decision makers, patients, and citizens. At a lower level, the same was found regarding the necessity to make value-judgments explicit in different report sections, including ethical analysis. This contrasts with the response-variability obtained on the status of ethical analysis with the exception of the expertise required. Variability in stakeholder-participation usefulness was also observed. CONCLUSIONS: This study reveals the importance of a three-phase approach, including assessment, contextual data, and recommendations, and highlights the necessity to make explicit value-judgments and have a systematic ethical analysis in order to fulfill HTA's social role in guiding decision makers.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".