The Big D(eal): professional identity through discursive constructions of ‘patient’
Bibliographic record
Abstract
CONTEXT: Professional identity formation has become a key focus for medical education. Who one becomes as a physician is contingent upon learning to conceptualise who the other is as a patient, yet, at a time when influential ideologies such as patient-centred care have become espoused values, there has been little empirical investigation into assumptions of 'patient' that trainees take up as they progress through their training. METHODS: Our team employed a critical discourse analysis approach to transcripts originally produced from a micro-ethnography of medical student learning on an acute care in-patient paediatric ward. The dataset included 20 case presentations and 14 sign-over rounds taken from a 3-week observation period. We paid specific attention to how trainees used language to talk about, refer to and categorise patients. RESULTS: Identified discourses included patient-as-disease-category, patient-as-educational-commodity and patient-as-marginalised-actor. These discourses conceptualise 'patient' as an entity that is principally biomedical, useful for clinical learning and spoken for and about. Medical student participation in these discourses contributes to an identity that allows them to move further into the professional medical world they are joining. CONCLUSIONS: We contend that as learners participate in these discourses, they are also performatively produced by them. By making these discourses visible, we can consider how to minimise unintended effects such discourses may cause. Our findings, although limited, offer a glimpse of the effects that those assumptions may have as we look to align better the formation of professional medical identity with the ideals of patient-centred care and socially responsible health care systems.
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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 | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".