Oncologists communicating with patients about assisted dying
Bibliographic record
Abstract
PURPOSE OF REVIEW: Across all jurisdictions in which assisted dying is legally permissible, cancer is the primary reported underlying diagnosis. Therefore, oncologists are likely to be asked about assisted dying and should be equipped to respond to inquiries or requests for assisted dying. Because Medical Assistance in Dying was legalized in Canada in 2016, it is a relatively new end-of-life practice and has prompted the need to revisit the academic literature to inform communication with patients about assisted dying. RECENT FINDINGS: We reviewed applicable literature published in the past 5 years, pertaining to assisted dying and communication. In total, 86 articles were identified, 21 were flagged as relevant to review in detail, and six were included in the review. Key themes included perceived barriers and benefits to communicating with patients on the topic, pragmatic approaches for facilitating the conversation with patients, and the issue of proactively discussing assisted dying by broaching it as an option with patients. SUMMARY: These findings indicate that there is still discomfort around having conversations about assisted dying with patients but new tools and approaches are being developed to support the practice.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".