The Ethics of Germline Gene Editing and Nursing Ethics
Bibliographic record
Abstract
This paper explores the current ethical issues in the potential use of germline gene therapy. This paper will also discuss the ethical principles of beneficence and non-maleficence in the context of germline therapy. The balance between potential benefits and potential harm in its use will be appraised. Moreover, the principle of autonomy will be further studied. More specifically the issue of consent and the potential dilemma when the modified individual’s will and those who chose the modifications do not align will be examined. Furthermore, the ethicality in the potential non-medical use of germline gene therapy will be investigated. Also, the consequences for the non-medical use in the therapy such as potential human rights violations and a breach in the ethical principle of justice will be speculated. Moreover, this paper highlights the use of the Canadian Nurses Association (CNA) Code of Ethics to help guide nurses through the complex ethical problems that they may face in germline gene therapy. Keywords: germline, gene therapy, germline editing, germline therapy, ethics, ethical principles, autonomy, beneficence, non-maleficence, justice, non-medical germline gene therapy, Canadian Nurses Association (CNA) Code of Ethics, CRISPR
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| 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".