ETHICS EXPERTISE FOR HEALTH TECHNOLOGY ASSESSMENT: A CANADIAN NATIONAL SURVEY
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to identify individuals with expertise in ethics analysis in Canada, who might contribute to health technology assessment (HTA); to gauge these individuals' familiarity with, and experience participating in, the production of HTA. METHODS: A contact list was developed using the Canadian Bioethics Society membership list and faculty listings of Canadian universities, bioethics centers, and health agencies. An eighteen-question email survey was distributed to potential respondents to collect data on demographic information, education and work experience in applied ethics, and involvement in HTA. RESULTS: The survey response rate was 52.8 percent (350/663). Respondents worked primarily in academic institutions (50.4 percent) or hospitals (15.4 percent). Many respondents (83.1 percent) had education, formal training, or work-related experience in practical ethics related to health care, with many having a doctorate (34.5 percent) or master's degree (19.0 percent). One quarter (24.5 percent; n = 87) of respondents indicated they had been involved in an analysis of ethical issues for HTA. Almost two-thirds (65.4 percent; n = 165) of those who had not previously participated in ethics analysis believed they might usefully contribute to an analysis of ethical issues in HTA. Experts who have conducted ethics analysis in HTA had more than twice the odds of having education and training in ethics and a PhD than those who might contribute to ethics analysis. CONCLUSION: Many people have contributed to ethics analysis in HTA in Canada, and more are willing to do so. Given the absence of a reliable credential for ethics expertise, HTA producers should exercise caution when enlisting ethics experts.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".