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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.061 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".