Bibliographic record
Abstract
BACKGROUND: Physicians face ethical difficulties daily, yet they seek ethics consultation infrequently. To date, no systematic data have been collected on the strategies they use to resolve such difficulties when they do so without the help of ethics consultation. Thus, our understanding of ethical decision making in day to day medical practice is poor. We report findings from the qualitative analysis of 310 ethically difficult situations described to us by physicians who encountered them in their practice. When facing such situations, the physicians sought to avoid conflict, obtain assistance, and protect the integrity of their conscience and reputation, as well as the integrity of the group of people who participated in the decisions. These goals could conflict with each other, or with ethical goals, in problematic ways. Being aware of these potentially conflicting goals may help physicians to resolve ethical difficulties more effectively. This awareness should also contribute to informing the practice of ethics consultation. OBJECTIVE: To identify strategies used by physicians in dealing with ethical difficulties in their practice. DESIGN, SETTING, AND PARTICIPANTS: National survey of internists, oncologists, and intensive care specialists by computer assisted telephone interviews (n = 344, response rate = 64%). As part of this survey, we asked physicians to tell us about a recent ethical dilemma they had encountered in their medical practice. Transcripts of their open-ended responses were analysed using coding and analytical elements of the grounded theory approach. MAIN MEASUREMENTS: Strategies and approaches reported by respondents as part of their account of a recent ethical difficulty they had encountered in their practice. RESULTS: When faced with ethical difficulties, the physicians avoided conflict and looked for assistance, which contributed to protecting, or attempting to protect, the integrity of their conscience and reputation, as well as the integrity of the group of people who participated in the decisions. These efforts sometimes reinforced ethical goals, such as following patients' wishes or their best interests, but they sometimes competed with them. The goals of avoiding conflict, obtaining assistance, and protecting the respondent's integrity and that of the group of decision makers could also compete with each other. CONCLUSION: In resolving ethical difficulties in medical practice, internists entertained competing goals that they did not always successfully achieve. Additionally, the means employed were not always the most likely to achieve those aims. Understanding these aspects of ethical decision making in medical practice is important both for physicians themselves as they struggle with ethical difficulties and for the ethics consultants who wish to help them in this process.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".