Bibliographic record
Abstract
There’s no doubt that medical ethics should be ‘grounded’, in the sense that it aims to make a practical, normative contribution to significant ethical issues in medicine. There are a number of ways in which ethics can do that, two of which feature in this issue of the Journal of Medical Ethics . One way is by responding to significant new policy or legal developments that will have an impact on clinical practice. This issue discusses two legal developments that matter to patients and healthcare professionals: the sanctions applied to Dr Bawa-Garba and the Supreme Court’s ruling on the withdrawal of artificial nutrition and hydration. A second way of grounding ethical analysis in the reality and complexity of ethical issues is by using empirical methods. There are two papers in this issue from Canada that illustrate how the subtleties of complex ethical issues can be teased out via qualitative methods. Medical tourism is an important and rapidly developing phenomenon that raises a set of interesting and tricky ethical issues.1 It has been discussed in the Journal of Medical Ethics before and its implications for end of life, dentistry and other health interventions have been explored.2 3 Reproductive tourism occurs in many countries and the complications it can create for issues such as the citizenship of resulting children have been discussed at some length in the JME.4 5 Reproductive tourism is a good example of an area where it is particularly important for ethical analysis to be grounded in the facts and reality of a situation and an empirical approach to ethics is therefore a good option for this topic. In this issue, Couture et …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.279 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.046 | 0.124 |
| 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; both teacher heads agree on what is shown here.
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".