Core competencies for ethics experts in health technology assessment
Bibliographic record
Abstract
OBJECTIVES: There is no consensus on who might be qualified to conduct ethical analysis in the field of health technology assessment (HTA). Is there a specific expertise or skill set for doing this work? The aim of this article is to (i) clarify the concept of ethics expertise and, based on this, (ii) describe and specify the characteristics of ethics expertise in HTA. METHODS: Based on the current literature and experiences in conducting ethical analysis in HTA, a group of members of the Health Technology Assessment International (HTAi) Interest Group on Ethical Issues in HTA critically analyzed the collected information during two face-to-face workshops. On the basis of the analysis, working definitions of "ethics expertise" and "core competencies" of ethics experts in HTA were developed. This paper reports the output of the workshop and subsequent revisions and discussions online among the authors. RESULTS: Expertise in a domain consists of both explicit and tacit knowledge and is acquired by formal training and social learning. There is a ubiquitous ethical expertise shared by most people in society; nevertheless, some people acquire specialist ethical expertise. To become an ethics expert in the field of HTA, one needs to acquire general knowledge about ethical issues as well as specific knowledge of the ethical domain in HTA. The core competencies of ethics experts in HTA consist of three fundamental elements: knowledge, skills, and attitudes. CONCLUSIONS: The competencies described here can be used by HTA agencies and others involved in HTA to call attention to and strengthen ethical analysis in HTA.
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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 | 0.028 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".