A Phenomenological Exploration of Physicians’ Moral Distress: Situating Emotion Within Lived Experiences
Bibliographic record
Abstract
PURPOSE: Physicians often experience moral distress from being prevented from taking what they believe to be the right course of action. Although causes and consequences of moral distress have been studied, little research offers insight into the significance of feeling morally challenged, especially in medicine. This study was undertaken to advance understanding of what physicians experience when encountering morally challenging situations and to examine how those experiences influence their interactions with the world of health care. METHOD: Guided by hermeneutic phenomenology, the authors conducted semistructured interviews with purposefully sampled Canadian physicians from September 2022 to January 2023. Analysis used Van Manen's 6 activities of hermeneutic research and included "story crafting" to sharpen insights. Late in that process, Van Manen's "lived existentials" was determined to provide a strong framework for physicians' experiences of moral distress. RESULTS: Of the 12 physicians interviewed, all experienced varied and long-lasting emotional reactions to morally challenging experiences. The emotions associated with moral distress, reported as inevitable, were experienced in ways that aligned with the lived existentials of body, space, time, and relations. CONCLUSIONS: A richer understanding of physicians' lived experiences with moral distress can help mentors and educators more thoughtfully engage in conversations about it with colleagues and learners. This study highlights the core dimensions of moral distress through body, space, time, and relations, thereby providing a means to enrich discussion about emotions and their transformational power. Through such nuanced consideration, calls for emotional candor may be strengthened to enhance practice by acknowledging the scope of impact that efforts to deliver care are likely to provoke.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".