How individual ethical frameworks shape physician trainees’ experiences providing end-of-life care: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: The end of life is an ethically challenging time requiring complex decision-making. This study describes ethical frameworks among physician trainees, explores how these frameworks manifest and relates these frameworks to experiences delivering end-of-life care. DESIGN: We conducted semistructured in-depth exploratory qualitative interviews with physician trainees about experiences of end-of-life care and moral distress. We analysed the interviews using thematic analysis. SETTING: Academic teaching hospitals in the United States and United Kingdom. PARTICIPANTS: We interviewed 30 physician trainees. We purposefully sampled across three domains we expected to be associated with individual ethics (stage of training, gender and national healthcare context) in order to elicit a diversity of ethical and experiential perspectives. RESULTS: Some trainees subscribed to a best interest ethical framework, characterised by offering recommendations consistent with the patient's goals and values, presenting only medically appropriate choices and supporting shared decision-making between the patient/family and medical team. Others endorsed an autonomy framework, characterised by presenting all technologically feasible choices, refraining from offering recommendations and prioritising the voice of patient/family as the decision-maker. CONCLUSIONS: This study describes how physician trainees conceptualise their roles as being rooted in an autonomy or best interest framework. Physician trainees have limited clinical experience and decision-making autonomy and may have ethical frameworks that are dynamic and potentially highly influenced by experiences providing end-of-life care. A better understanding of how individual physicians' ethical frameworks influences the care they give provides opportunities to improve patient communication and advance the role of shared decision-making to ensure goal-aligned end-of-life care.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".