BARRIERS AND FACILITATORS INFLUENCING ETHICAL EVALUATION IN HEALTH TECHNOLOGY ASSESSMENT
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to explore barriers and facilitators influencing the integration of ethical considerations in health technology assessment (HTA). METHODS: The study consisted of two complementary approaches: (a) a systematic review of the literature; and (b) an eighteen-item online survey that was distributed to fifty-six HTA agencies affiliated with the International Network of Agencies for Health Technology Assessment. RESULTS: The review identified twenty-six relevant articles. The most often cited barriers in the literature were: scarcity, heterogeneity and complexity of ethical analysis methods; challenges in translating ethical analysis results into knowledge that is useful for decision makers; and lack of organizational support in terms of required expertise, time and financial resources. The most frequently cited facilitators included: usage of value-based appraisal methods, stakeholder and public engagement, enhancement of practice guidelines, ethical expertise, and educational interventions. Representatives of twenty-six (46.5 percent) agencies from nineteen countries completed the survey. A median of 10 percent (interquartile range, 5 percent to 50 percent) of the HTA products produced by the agencies was reported to include an assessment of ethical aspects. The most commonly perceived barriers were: limited ethical knowledge and expertise, insufficient time and resources, and difficulties in finding ethical evidence or using ethical guidelines. Educational interventions, demand by policy makers, and involvement of ethicists in HTA were the most commonly perceived facilitators. CONCLUSIONS: Our results emphasize the importance of simplification of ethics methodology and development of good practice guidelines in HTA, as well as capacity building for engaging HTA practitioners in ethical analyses.
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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 | 0.294 | 0.496 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".