Truth-telling and doctor-assisted death as perceived by Israeli physicians
Bibliographic record
Abstract
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 patientsâ 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.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".