The Disconnect of Twin Pillars: The Growing Rift in Educational Goals and Methods between Medical Schools and the Academic Teaching Hospitals
Bibliographic record
Abstract
Academic teaching hospitals (ATH) and medical schools are the two main components of the Academic Health Sciences Centre (AHSC) organization. They have traditionally worked in a symbiotic, f relatively unstructured and somewhat fluid relationship. Now changes in the medical school approach are creating stress on this traditional partnership. First, medical schools are being driven by external pressures to better respond to societal needs. Medical schools are increasingly decentralizing their educational process to help produce physicians with the values and skills needed to meet the diverse needs of Canadian society. Second, internally, the changing nature of medical knowledge and skill sets has led to differences in the educational process with more formal standards and educational goals. Within this second change is a difference in the trainees moving through the educational system - today's future doctors represent a different value set and demographic profile than their predecessors. These changes pose both a challenge and an opportunity for ATHs. ATHs are well positioned to be leaders and facilitators of these changes. Doing so would help strengthen the system, and would ultimately help ATHs fulfil their complex and often competing mandates. Unfortunately, there are also incentives for ATHs to fight these trends. The response of ATHs to their evolving relationship with medical schools and universities will have a large influence on the future shape and function of the AHSC.
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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 | 0.037 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".