What makes a ‘good doctor’? A critical discourse analysis of perspectives from medical students with lived experience as patients
Bibliographic record
Abstract
What constitutes a 'good doctor' varies widely across groups and contexts. While patients prioritise communication and empathy, physicians emphasise medical expertise, and medical students describe a combination of the two as professional ideals. We explored the conceptions of the 'good doctor' held by medical learners with chronic illnesses or disabilities who self-identify as patients to understand how their learning as both patients and future physicians aligns with existing medical school curricula. We conducted 10 semistructured interviews with medical students with self-reported chronic illness or disability and who self-identified as patients. We used critical discourse analysis to code for dimensions of the 'good doctor'. In turn, using concepts of Bakhtinian intersubjectivity and the hidden curriculum we explored how these discourses related to student experiences with formal and informal curricular content.According to participants, dimensions of the 'good doctor' included empathy, communication, attention to illness impact and boundary-setting to separate self from patients. Students reported that formal teaching on empathy and illness impact were present in the formal curriculum, however ultimately devalued through day-to-day interactions with faculty and peers. Importantly, teaching on boundary-setting was absent from the formal curriculum, however participants independently developed reflective practices to cultivate these skills. Moreover, we identified two operating discourses of the 'good doctor': an institutionalised discourse of the 'able doctor' and a counterdiscourse of the 'doctor with lived experience' which created a space for reframing experiences with illness and disability as a source of expertise rather than a source of stigma. Perspectives on the 'good doctor' carry important implications for how we define professional roles, and hold profound consequences for medical school admissions, curricular teaching and licensure. Medical students with lived experiences of illness and disability offer critical insights about curricular messages of the 'good doctor' based on their experiences as patients, providing important considerations for curriculum and faculty development.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| 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".