Scientist or science‐stuffed? Discourses of science in North American medical education
Bibliographic record
Abstract
CONTEXT: The dominance of biomedical science in medical education has been contested throughout the past century, with recurring calls for more social science and humanities content. The centrality of biomedicine is frequently traced back to Abraham Flexner's 1910 report, 'Medical Education in the United States and Canada'. However, Flexner advocated for a scientist-doctor, rather than a curriculum filled with science content. Examination of the discourses of science since Flexner allows us to explore the place of various knowledge forms in medical education. METHODS: A Foucauldian critical discourse analysis was performed, examining the discourses of scientific medicine in Flexner's works and North American medical education articles in subsequent decades. Foucault's methodological principles were used to identify statements, keywords and metaphors that emerged in the development of the discourses of scientific medicine, with particular attention to recurring arguments and shifts in the meaning and use of terms. RESULTS: Flexner's scientist-doctor was an incisive thinker who drew upon multiple forms of knowledge. In the post-Flexner medical education reforms, the perception of science as a discursive object embedded in the curriculum became predominant over that of the scientist as the discursive subject who uses science. Science was then considered core curricular content and was discursively framed as impossibly vast. A parallel discourse, one of the insufficiency of biomedical science for the proper training of doctors, has existed over the past century, even as the humanities and social sciences have remained on the margins in medical school curricula. CONCLUSIONS: That discourses of scientific medicine have reinforced the centrality of biomedicine in medical education helps to explain the persistent marginalisation of other important knowledge domains. Medical educators need to be aware of the effects of these discourses on understandings of medical knowledge, particularly when contemplating curricular reform.
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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.027 | 0.058 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".