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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.015 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".