Bibliographic record
Abstract
CONTEXT: Twentieth-century medical education constructed medicine as biomedical science. Although bioscientific knowledge has brought large benefits to clinical practice, many have questioned the appropriateness of its domination of the medical curriculum. As the content of that curriculum is itself a historically mediated social construct, it can be changed to fit current descriptions of the competent doctors medical schools are expected to produce. Such doctors are expected not only to have biomedical expertise, but also to carry out multiple other roles as described in competency frameworks such as that of CanMEDS. Many of these other roles are socio-culturally based and thus not supported by bioscientific knowledge. METHODS: We designed a thought experiment to delineate the need to identify and integrate the range of foundational knowledges required to support the development of doctors capable of performing all the roles described in the competency frameworks. We specified assumptions and demarcated our scope. To illustrate our ideas, we selected examples from the medical curriculum that linked to non-Medical Expert roles and outlined the disciplines that supported them. RESULTS: Students educated in the foundational knowledge necessary for competence in all doctor roles would need to be exposed to ideas and ways of thinking from a wide array of disciplines outside the traditional biomedical sciences. These would need to be introduced in context and in ways that would support future medical practice. They would also broaden students' understanding of the nature of legitimate medical knowledge. CONCLUSIONS: There are currently major gaps between the goals and objectives of competency frameworks such as CanMEDS and the actual contents of medical curricula. Addressing these will require curricular transformation to add knowledges, in context and in ways that positively affect practice, from disciplines not currently present within the medical school. In order to accomplish this, we will need to engage with colleagues throughout the university.
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 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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".