Rockefeller Foundation Funding and Medical Education in Toronto, Montreal and Halifax
Bibliographic record
Abstract
ROCKEFELLER FOUNDATION FUNDING AND MEDICAL EDUCATION IN TORONTO, MONTREAL AND HALIFAX Marianne P. Fedunkiw Montreal & Kingston: McGill-Queen's University Press, 2005. 201pp, $47.25 cloth (ISBN 0773526970)Currently there is much discussion in Canada about the need to reform medical education. However, there is little discussion about embedded traditions of the past, how these came into play, and finally how the interplay of ideas, interests, resources, and institutions shaped the pace and direction of change. Marianne P. Fedunkiw fills some of this gap, and in doing so provides a insightful and well-written overview of the trials and tribulations connected with advancing medical education and research in Toronto, Montreal, and Halifax. The first section of the book provides an overview of the scientization of medicine, as well as the drivers and constraints that shaped events in our country. It is suggested that the Flexner report, which was a major driver in the United States, was not as significant in Canada. Put simply, it is a Canadian account of the scientization movement that focused much attention on the role played by the Rockefeller Foundation and the Eaton family in institutionalizing a new approach across provincial jurisdictions and institutions. As one would expect in a diverse federal system, the movement did not unfold the same way in each province or medical school. The rest of the book deals with the different responses to these external attempts to push new agendas and institutionalize new models. In each case study, emphasis is placed on explaining how these external initiatives influenced the process of change, both the opportunities and challenges that shaped decision-making, and, finally, the outcomes achieved in three very different contextual settings.This is a work that will appeal to different audiences, and Fedunkiw is an effective commentator and storyteller. It has never been easy moving reforms along a common universal path within a highly competitive federation, getting a consensus on priorities, striking an appropriate balance between practical and researched skills, agreeing on what should be taught, by whom and how it should be financed, levels and degrees of autonomy for universities, the role to be played by affiliated professional groups, and so on. These are common questions and challenges for professional schools, whether in the area of medicine, law, engineering, or business. The book offers a number of critical insights on the challenges of health care reform across institutions and provinces but these can also be applied in other policy fields.When universities play a significant role in generating new partnerships that encourage entrepreneurial work, new forms of knowledge, and better practices in service delivery, it is important to understand how this was achieved in the past. …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.013 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.101 | 0.018 |
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".