Ethics education for clinician–researchers in genetics: The combined approach
Bibliographic record
Abstract
Advancements in genomic technology and genetic research have uncovered new and unforeseen ethical and legal issues that must now be faced by clinician-researchers. However, lack of adequate ethical training places clinician-researchers in a position where they might be unable to effectively assess and resolve the issues presented to them. The literature demonstrates that ethics education is relevant and engaging where it is targeted to the level and context of the learners, and it includes real-world based cases approached in innovative ways. In order to test the feasibility of a combined approach to ethics education, a conference was held in 2012 to raise awareness and familiarize participants with the ethical and legal issues surrounding medical technology in genetics and then to have them apply this to reality-based case studies. The conference included participants from a variety of backgrounds and was divided into three sections: (i) informative presentations by experts in the field; (ii) mock REB deliberations; and (iii) a second mock-REB, conducted by a panel of experts. Feedback from participants was positive and indicated that they felt the learning objectives had been met and that the material was presented in a clear and organized fashion. Although only an example of the combined approach in a particular setting, the success of this conference suggests that combining small group learning, practical cases, role-play and interdisciplinary learning provides a positive experience and is an effective approach to ethics education.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| 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".