From challenge to growth: Exploring physician narratives of patient complaints during residency
Bibliographic record
Abstract
INTRODUCTION: Patient complaints are an important feedback mechanism for healthcare quality improvement and medical education, and their impact on postgraduate medical trainees (residents) remains under-explored. This study investigates the narratives of physicians who received formal patient complaints during residency, focusing on how these incidents influenced their professional development and career trajectories. METHODS: Using narrative inquiry, we conducted semi-structured interviews with 35 physicians who experienced formal patient complaints during their residency. We applied Ricoeur's narrative theory to explore the impact of these complaints on physicians. RESULTS: The analysis revealed two main elements of participants' narratives: emplotment and refiguration. During emplotment, participants initially constructed narratives centered on distress and stigma, often positioning themselves as confused and isolated. This process frequently involved a reevaluation of their professional capabilities and identity as an effective physician. Refiguration demonstrated how participants integrated these experiences into their long-term professional identities, revealing impacts on clinical practices, specialty choices and career paths. Despite initial challenges, many participants reframed their early complaint experiences as catalysts for professional growth, particularly in areas such as communication and collegial support. CONCLUSIONS: Patient complaints during residency can have a destabilizing effect on professional identity formation. However, supportive educational environments and adequate mentorship can mitigate these effects and enhance learning. This study underscores the need for medical education programmes to incorporate systems that support residents in effectively addressing and learning from patient complaints while maintaining a focus on patient safety and quality improvement.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".