What triggers requests for ethics consultations?
Bibliographic record
Abstract
OBJECTIVES: While clinical practice is complicated by many ethical dilemmas, clinicians do not often request ethics consultations. We therefore investigated what triggers clinicians' requests for ethics consultation. DESIGN: Cross-sectional telephone survey. SETTING: Internal medicine practices throughout the United States. PARTICIPANTS: Randomly selected physicians practising in internal medicine, oncology and critical care. MAIN MEASUREMENTS: Socio-demographic characteristics, training in medicine and ethics, and practice characteristics; types of ethical problems that prompt requests for consultation, and factors triggering consultation requests. RESULTS: One hundred and ninety of 344 responding physicians (55%) reported requesting ethics consultations. Physicians most commonly reported requesting ethics consultations for ethical dilemmas related to end-of-life decision making, patient autonomy issues, and conflict. The most common triggers that led to consultation requests were: 1) wanting help resolving a conflict; 2) wanting assistance interacting with a difficult family, patient, or surrogate; 3) wanting help making a decision or planning care, and 4) emotional triggers. Physicians who were ethnically in the minority, practised in communities under 500,000 population, or who were trained in the US were more likely to request consultations prompted by conflict. CONCLUSIONS: Conflicts and other emotionally charged concerns triggers consultation requests more commonly than other cognitively based concerns. Ethicists need to be prepared to mediate conflicts and handle sometimes difficult emotional situations when consulting. The data suggest that ethics consultants might serve clinicians well by consulting on a more proactive basis to avoid conflicts and by educating clinicians to develop mediation skills.
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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 | 0.006 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".