Director of Diagnostic Laboratories and Pathobiology, St. Michael ’ s Hospital, Toronto
Bibliographic record
Abstract
Academic Health Sciences Centres (AHSCs) are an enduring feature of health systems in all developed countries. In Canada, despite the lack of precise definition and standard-ized organizational arrangements, the educational services and programs in health sciences offered by AHSCs, and the caregiving organizations they embrace, are critical components of the national health system. Yet, the past decade has been a period of profound change in the Canadian health system. The pace of this change and the nature of the demands on the system are unlikely to abate in the near future. Given that many of these changes have directly impacted on AHSCs, or their component parts, it is timely to review these entities and to understand more fully how these organizations have been, or may be, affected in the future. This paper identifies many of the unique attributes of AHSCs that have arisen from their threefold mission of patient care, teaching and research. The authors describe many of the most critical issues confronting AHSCs in the current era: diminishing financial support;increasing demands on the system, with little prospect of new resources; new forms of care delivery such as regional models; alternative plans for physician compensa-tion; and new models for research funding.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.144 | 0.023 |
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".