Stethoscope of the 21st century: dominant discourses of ultrasound in medical education
Bibliographic record
Abstract
CONTEXT: In recent years, point-of-care ultrasound (POCUS) has become a widely used clinical tool in a number of clinical specialties. In response, POCUS has been incorporated into medical curricula across the learning continuum, bolstered by enthusiastic appraisals of the technology's benefits for learners, clinicians and patients. In this project, we have sought to identify and understand the effects of dominant discourses influencing the integration of POCUS into medical education. METHODS: We conducted a Foucauldian critical discourse analysis (CDA) to identify and analyse discourses that legitimise and privilege the use of POCUS in medical education. We assembled an archive of 473 texts published between 1980 and 2017. Each article in the archive was analysed to identify frequently occurring truth statements (expressing concepts whose truths are unquestioned within particular discourses) that we used to characterise the major discourses that construct representations of POCUS in medical education. RESULTS: We identified three dominant discourses: (i) a visuo-centric discourse prioritising the visual information as truth over other clinical data; (ii) a utilitarian discourse emphasising improvements in patient care; and (iii) a modernist discourse highlighting the current and future needs of clinicians in our technological world. These discourses overlap and converge; the core discursive effect makes the further elevation of POCUS in medical education, and the resulting attenuation of other curricular priorities, appear inevitable. CONCLUSIONS: The three dominant discourses identified in this paper engender ideal conditions for the proliferation of POCUS in medical education through curricular guidelines, surveys of adherence to these guidelines and authoritative position statements. By identifying and analysing these dominant discourses, we can ask questions that do not take for granted the assumed truths underpinning the discourses, highlight potential pitfalls of proposed curricular changes and ensure these changes truly improve medical education.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".