Truth-telling and doctor-assisted death as perceived by Israeli physicians
Bibliographic record
Abstract
BACKGROUND: Medicine has undergone substantial changes in the way medical dilemmas are being dealt with. Here we explore the attitude of Israeli physicians to two debatable dilemmas: disclosing the full truth to patients about a poor medical prognosis, and assisting terminally ill patients in ending their lives. METHODS: Attitudes towards medico-ethical dilemmas were examined through a nationwide online survey conducted among members of the Israeli Medical Association, yielding 2926 responses. RESULTS: Close to 60% of the respondents supported doctor-assisted death, while one third rejected it. Half of the respondents opposed disclosure of the full truth about a poor medical prognosis, and the others supported it. Support for truth-telling was higher among younger physicians, and support for doctor-assisted death was higher among females and among physicians practicing in hospitals. One quarter of respondents supported both truth-telling and assisted death, thereby exhibiting respect for patients' autonomy. This approach characterizes younger doctors and is less frequent among general practitioners. Another quarter of the respondents rejected truth-telling, yet supported assisted death, thereby manifesting compassionate pragmatism. This was associated with medical education, being more frequent among doctors educated in Israel, than those educated abroad. All this suggests that both personal attributes and professional experience affect attitudes of physicians to ethical questions. CONCLUSIONS: Examination of attitudes to two debatable medical dilemmas allowed portrayal of the multi-faceted medico-ethical scene in Israel. Moreover, this study, demonstrates that one can probe the ethical atmosphere of a given medical community, at various time points by using a few carefully selected questions.
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 | 0.017 | 0.229 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".